From 5036ec9a076c4dde9555670fcc640e4b1d9396ae Mon Sep 17 00:00:00 2001 From: githubtemp5 Date: Sat, 7 Sep 2024 18:35:13 +0100 Subject: [PATCH] progress in learning --- .../2-Data/01 - Flights Challenge.ipynb | 213 +- 2-Regression/3-Linear/Housing.csv | 546 +++ 2-Regression/3-Linear/README.md | 3 +- 2-Regression/3-Linear/assignment.ipynb | 1984 +++++++++++ 2-Regression/3-Linear/image.png | Bin 0 -> 42788 bytes 2-Regression/3-Linear/notebook.ipynb | 1707 +++++++++- 2-Regression/4-Logistic/assignment.ipynb | 620 ++++ 2-Regression/4-Logistic/notebook.ipynb | 935 ++++- .../penguins_binary_classification.csv | 275 ++ 3-Web-App/1-Web-App/notebook.ipynb | 376 ++ 3-Web-App/1-Web-App/ufo-model.pkl | Bin 0 -> 1011 bytes 3-Web-App/1-Web-App/web-app/app.py | 32 + 3-Web-App/1-Web-App/web-app/requirements.txt | 4 + .../1-Web-App/web-app/static/css/styles.css | 29 + .../1-Web-App/web-app/templates/index.html | 30 + 3-Web-App/1-Web-App/web-app/ufo-model.pkl | Bin 0 -> 1011 bytes .../1-Introduction/notebook.ipynb | 796 ++++- 4-Classification/data/cleaned_cuisines.csv | 3032 ++++++++--------- 18 files changed, 9032 insertions(+), 1550 deletions(-) create mode 100644 2-Regression/3-Linear/Housing.csv create mode 100644 2-Regression/3-Linear/assignment.ipynb create mode 100644 2-Regression/3-Linear/image.png create mode 100644 2-Regression/4-Logistic/assignment.ipynb create mode 100644 2-Regression/4-Logistic/penguins_binary_classification.csv create mode 100644 3-Web-App/1-Web-App/ufo-model.pkl create mode 100644 3-Web-App/1-Web-App/web-app/app.py create mode 100644 3-Web-App/1-Web-App/web-app/requirements.txt create mode 100644 3-Web-App/1-Web-App/web-app/static/css/styles.css create mode 100644 3-Web-App/1-Web-App/web-app/templates/index.html create mode 100644 3-Web-App/1-Web-App/web-app/ufo-model.pkl diff --git a/2-Regression/2-Data/01 - Flights Challenge.ipynb b/2-Regression/2-Data/01 - Flights Challenge.ipynb index 17a653b82..e4c2ddcec 100644 --- a/2-Regression/2-Data/01 - Flights Challenge.ipynb +++ b/2-Regression/2-Data/01 - Flights Challenge.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -163,7 +163,7 @@ "(269167, 20)" ] }, - "execution_count": 81, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -197,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -216,7 +216,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\billg\\AppData\\Local\\Temp\\ipykernel_12428\\3811281340.py:40: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + "C:\\Users\\billg\\AppData\\Local\\Temp\\ipykernel_14268\\3811281340.py:40: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", " fig.show()\n" ] }, @@ -256,7 +256,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\billg\\AppData\\Local\\Temp\\ipykernel_12428\\3811281340.py:40: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + "C:\\Users\\billg\\AppData\\Local\\Temp\\ipykernel_14268\\3811281340.py:40: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", " fig.show()\n" ] }, @@ -359,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -504,7 +504,7 @@ "MQ 13.335253" ] }, - "execution_count": 110, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -523,7 +523,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -599,7 +599,7 @@ "4 10.827256" ] }, - "execution_count": 106, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -617,7 +617,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -1090,7 +1090,7 @@ "[12660 rows x 20 columns]" ] }, - "execution_count": 120, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1118,6 +1118,193 @@ "Chicago O'Hare International is the aiport with the highert departure delay" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Correlation late departure and late arrival" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "On-time Departures:16521\n", + "Delayed Departures:111759\n", + "0.9545551003011256\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "onTimeDepartures = df_flights.query(\"DepDelay == 0\")\n", + "print(f\"On-time Departures:{onTimeDepartures.DepDelay.count()}\")\n", + "delayedDepartures = df_flights.query(\"DepDelay > 0\")\n", + "print(f\"Delayed Departures:{delayedDepartures.DepDelay.count()}\")\n", + "\n", + "onTimeDepartures.boxplot(column='ArrDelay', by='DepDelay', figsize=(8,5))\n", + "delayedDepartures.plot.scatter(title='Delayed Departures vs Arrival', x='DepDelay', y='ArrDelay')\n", + "print(delayedDepartures.DepDelay.corr(delayedDepartures.ArrDelay))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Year 2013\n", + "Month 4\n", + "DayofMonth 21\n", + "DayOfWeek 7\n", + "Carrier WN\n", + "OriginAirportID 12892\n", + "OriginAirportName Los Angeles International\n", + "OriginCity Los Angeles\n", + "OriginState CA\n", + "DestAirportID 14771\n", + "DestAirportName San Francisco International\n", + "DestCity San Francisco\n", + "DestState CA\n", + "CRSDepTime 850\n", + "DepDelay 45\n", + "DepDel15 1.0\n", + "CRSArrTime 1010\n", + "ArrDelay 31\n", + "ArrDel15 1\n", + "Cancelled 0\n", + "Name: 564, dtype: object" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lateArrivals = df_flights.query(\"ArrDelay > 0\")\n", + "lateArrivals = lateArrivals[[\"OriginAirportID\", \"DestAirportID\", \"ArrDelay\"]]\n", + "pd.set_option(\"display.max_rows\", None)\n", + "\n", + "lateArrivals.groupby([\"OriginAirportID\", \"DestAirportID\"]).count().sort_values(by=\"ArrDelay\", ascending=False)\n", + "\n", + "df_flights.query(\"(OriginAirportID == 12892 and DestAirportID == 14771)\").iloc[0]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
OriginAirportIDDestAirportIDArrDelay
1110821052912892225
\n", + "
" + ], + "text/plain": [ + " OriginAirportID DestAirportID ArrDelay\n", + "111082 10529 12892 225" + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lateArrivals.groupby([\"OriginAirportID\", \"DestAirportID\"]).mean().sort_values(by=\"ArrDelay\", ascending=False)\n", + "\n", + "#only 1 data point\n", + "lateArrivals.query(\"(OriginAirportID == 10529 and DestAirportID == 12892)\").sort_values(by=\"ArrDelay\", ascending=False)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "mean(Departure Delay): 10.40\n", + "mean(Arrival Delay):6.51\n", + "\n", + "AS is the carrier with the least departure delays and least arrival delays\n", + "MQ is the carrier with the most departure delays and least arrival delays\n", + "\n", + "Saturday is the day with the least arrival delays with 2.152108 minutes\n", + "Thursday is the day with the most arrival delays with 10.827256 minutes\n", + "So yes, there is a noticable difference\n", + "\n", + "Chicago O'Hare International is the airport with the highest average departure delay.\n", + "\n", + "Around 75% of flights that departed on time arrived before arrival time or a few minutes after arrival time.\n", + "\n", + "With a strong corelation of 0.9545 for delayed departures, it does result in longer delayed arrivals.\n", + "\n", + "Los Angeles International to San Francisco International was the route with the most late arrivals.\n", + "\n", + "Bradley International to Los Angeles International has the highest average arrival delay of 225 minutes. Although there is one one data point for these airports.\n", + "\n", + "\n", + "\n" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1169,7 +1356,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ diff --git a/2-Regression/3-Linear/Housing.csv b/2-Regression/3-Linear/Housing.csv new file mode 100644 index 000000000..88ca47836 --- /dev/null +++ b/2-Regression/3-Linear/Housing.csv @@ -0,0 +1,546 @@ +price,area,bedrooms,bathrooms,stories,mainroad,guestroom,basement,hotwaterheating,airconditioning,parking,prefarea,furnishingstatus +13300000,7420,4,2,3,yes,no,no,no,yes,2,yes,furnished +12250000,8960,4,4,4,yes,no,no,no,yes,3,no,furnished +12250000,9960,3,2,2,yes,no,yes,no,no,2,yes,semi-furnished +12215000,7500,4,2,2,yes,no,yes,no,yes,3,yes,furnished +11410000,7420,4,1,2,yes,yes,yes,no,yes,2,no,furnished +10850000,7500,3,3,1,yes,no,yes,no,yes,2,yes,semi-furnished 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areabedroomsbathroomsstoriesmainroadguestroombasementhotwaterheatingairconditioningparkingprefareafurnishingstatus
07420423TrueFalseFalseFalseTrue2Truefurnished
18960444TrueFalseFalseFalseTrue3Falsefurnished
29960322TrueFalseTrueFalseFalse2Truesemi-furnished
37500422TrueFalseTrueFalseTrue3Truefurnished
47420412TrueTrueTrueFalseTrue2Falsefurnished
\n", + "
" + ], + "text/plain": [ + " area bedrooms bathrooms stories mainroad guestroom basement \\\n", + "0 7420 4 2 3 True False False \n", + "1 8960 4 4 4 True False False \n", + "2 9960 3 2 2 True False True \n", + "3 7500 4 2 2 True False True \n", + "4 7420 4 1 2 True True True \n", + "\n", + " hotwaterheating airconditioning parking prefarea furnishingstatus \n", + "0 False True 2 True furnished \n", + "1 False True 3 False furnished \n", + "2 False False 2 True semi-furnished \n", + "3 False True 3 True furnished \n", + "4 False True 2 False furnished " + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "for column in [\"mainroad\",\"guestroom\",\"basement\", \"hotwaterheating\",\"airconditioning\", \"prefarea\"]:\n", + " X[column] = X[column] == \"yes\"\n", + "\n", + "X.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.5359973457780801)" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "area = X.area\n", + "\n", + "plt.scatter(area, y)\n", + "\n", + "area.corr(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.3664940257738684)" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X\n", + "bedrooms = X.bedrooms\n", + "\n", + "plt.bar(bedrooms, y)\n", + "bedrooms.corr(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.4207123661886168)" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X\n", + "stories = X.stories\n", + "plt.bar(stories, y)\n", + "\n", + "stories.corr(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X\n", + "mainroad = X.mainroad\n", + "\n", + "plt.bar(mainroad, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.3843936486357261)" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X\n", + "\n", + "parking = X.parking\n", + "\n", + "plt.bar(parking, y)\n", + "\n", + "parking.corr(y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One input regression model: area and price" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "area = area.to_numpy().reshape(-1,1)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "y = y.to_numpy().reshape(-1,1)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import mean_squared_error\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "XTrain, XTest, yTrain, yTest = train_test_split(area, y, test_size=0.2, random_state=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lin_reg = LinearRegression()\n", + "lin_reg.fit(XTrain, yTrain)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean error: 1.41e+06 (29.6%)\n" + ] + } + ], + "source": [ + "pred = lin_reg.predict(XTest)\n", + "\n", + "mse = np.sqrt(mean_squared_error(yTest,pred))\n", + "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(XTest, yTest)\n", + "plt.plot(XTest,pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Polynomial Regression on Area" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Pipeline(steps=[('polynomialfeatures', PolynomialFeatures()),\n",
+       "                ('linearregression', LinearRegression())])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures()),\n", + " ('linearregression', LinearRegression())])" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.preprocessing import PolynomialFeatures\n", + "from sklearn.pipeline import make_pipeline\n", + "\n", + "pipeline = make_pipeline(PolynomialFeatures(2), LinearRegression())\n", + "\n", + "pipeline.fit(XTrain, yTrain)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean error: 2.06e+06 (43.2%)\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " area bedrooms bathrooms stories mainroad guestroom basement \\\n", + "0 7420 4 2 3 True False False \n", + "1 8960 4 4 4 True False False \n", + "2 9960 3 2 2 True False True \n", + "3 7500 4 2 2 True False True \n", + "4 7420 4 1 2 True True True \n", + "\n", + " hotwaterheating airconditioning parking prefarea furnishingstatus \n", + "0 False True 2 True furnished \n", + "1 False True 3 False furnished \n", + "2 False False 2 True semi-furnished \n", + "3 False True 3 True furnished \n", + "4 False True 2 False furnished " + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "features = pd.get_dummies(X.furnishingstatus).join(X.mainroad).join(X.guestroom).join(X.basement).join(X.hotwaterheating).join(X.airconditioning).join(X.prefarea) \\\n", + ".join(X.bedrooms).join(X.bathrooms).join(X.stories)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean error: 9.79e+14 (1.15e+03%)\n", + "Model determination: 0.8929338493561743\n" + ] + } + ], + "source": [ + "XTrain, XTest, yTrain, yTest = train_test_split(features, y, test_size=0.2, random_state=0)\n", + "\n", + "pipeline = make_pipeline(PolynomialFeatures(4), LinearRegression())\n", + "pipeline.fit(XTrain,yTrain)\n", + "\n", + "# predict results for test data\n", + "pred = pipeline.predict(XTest)\n", + "\n", + "# calculate MSE and determination\n", + "mse = np.sqrt(mean_squared_error(yTest,pred))\n", + "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n", + "\n", + "score = pipeline.score(XTrain,yTrain)\n", + "print('Model determination: ', score)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + 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City NameTypePackageVarietySub VarietyGradeDateLow PriceHigh PriceMostly Low...Unit of SaleQualityConditionAppearanceStorageCropRepackTrans ModeUnnamed: 24Unnamed: 25
0BALTIMORENaN24 inch binsNaNNaNNaN4/29/17270.0280.0270.0...NaNNaNNaNNaNNaNNaNENaNNaNNaN
1BALTIMORENaN24 inch binsNaNNaNNaN5/6/17270.0280.0270.0...NaNNaNNaNNaNNaNNaNENaNNaNNaN
2BALTIMORENaN24 inch binsHOWDEN TYPENaNNaN9/24/16160.0160.0160.0...NaNNaNNaNNaNNaNNaNNNaNNaNNaN
3BALTIMORENaN24 inch binsHOWDEN TYPENaNNaN9/24/16160.0160.0160.0...NaNNaNNaNNaNNaNNaNNNaNNaNNaN
4BALTIMORENaN24 inch binsHOWDEN TYPENaNNaN11/5/1690.0100.090.0...NaNNaNNaNNaNNaNNaNNNaNNaNNaN
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\n", + "" + ], + "text/plain": [ + " City Name Type Package Variety Sub Variety Grade Date \\\n", + "0 BALTIMORE NaN 24 inch bins NaN NaN NaN 4/29/17 \n", + "1 BALTIMORE NaN 24 inch bins NaN NaN NaN 5/6/17 \n", + "2 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 9/24/16 \n", + "3 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 9/24/16 \n", + "4 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 11/5/16 \n", + "\n", + " Low Price High Price Mostly Low ... Unit of Sale Quality Condition \\\n", + "0 270.0 280.0 270.0 ... NaN NaN NaN \n", + "1 270.0 280.0 270.0 ... NaN NaN NaN \n", + "2 160.0 160.0 160.0 ... NaN NaN NaN \n", + "3 160.0 160.0 160.0 ... NaN NaN NaN \n", + "4 90.0 100.0 90.0 ... NaN NaN NaN \n", + "\n", + " Appearance Storage Crop Repack Trans Mode Unnamed: 24 Unnamed: 25 \n", + "0 NaN NaN NaN E NaN NaN NaN \n", + "1 NaN NaN NaN E NaN NaN NaN \n", + "2 NaN NaN NaN N NaN NaN NaN \n", + "3 NaN NaN NaN N NaN NaN NaN \n", + "4 NaN NaN NaN N NaN NaN NaN \n", + "\n", + "[5 rows x 26 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", @@ -32,9 +239,129 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\billg\\AppData\\Local\\Temp\\ipykernel_5488\\2637987050.py:9: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", + " day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n" + ] + }, + { + "data": { + "text/html": [ + "
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MonthDayOfYearVarietyCityPackageLow PriceHigh PricePrice
709267PIE TYPEBALTIMORE1 1/9 bushel cartons15.015.013.636364
719267PIE TYPEBALTIMORE1 1/9 bushel cartons18.018.016.363636
7210274PIE TYPEBALTIMORE1 1/9 bushel cartons18.018.016.363636
7310274PIE TYPEBALTIMORE1 1/9 bushel cartons17.017.015.454545
7410281PIE TYPEBALTIMORE1 1/9 bushel cartons15.015.013.636364
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" + ], + "text/plain": [ + " Month DayOfYear Variety City Package Low Price \\\n", + "70 9 267 PIE TYPE BALTIMORE 1 1/9 bushel cartons 15.0 \n", + "71 9 267 PIE TYPE BALTIMORE 1 1/9 bushel cartons 18.0 \n", + "72 10 274 PIE TYPE BALTIMORE 1 1/9 bushel cartons 18.0 \n", + "73 10 274 PIE TYPE BALTIMORE 1 1/9 bushel cartons 17.0 \n", + "74 10 281 PIE TYPE BALTIMORE 1 1/9 bushel cartons 15.0 \n", + "\n", + " High Price Price \n", + "70 15.0 13.636364 \n", + "71 18.0 16.363636 \n", + "72 18.0 16.363636 \n", + "73 17.0 15.454545 \n", + "74 15.0 13.636364 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n", "\n", @@ -71,9 +398,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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uuOMO/PrXvwYALF26FB999BE2btwIs9mM5cuXQ6/X47PPPhP1vlxqS90pKq/C6p0VXne4tZqNyM9JZ5MxDbjpZ7vQ0s3d4wwROvzzpVkhyIikuv2VfTh3pWvBmJoQjeInp4QgI1KboC+1ra+vx/z581FYWIjrrrvOs91ut+Ptt9/Gr371K0yZMgXjxo3Dhg0b8Ne//hWHDh0K5J8iQlF5FZZuPuZVeABAtd2JpZuPoai8KkSZkRi+Cg8AaGkXcNPPdimcEUnlq/AAgHNXmnD7K/sUzoi0LqDiY9myZZg9ezamTZvmtf3o0aNobW312j5ixAikpKSgtLS0d5lSn9TuErB6ZwW6++pyb1u9s4KXYFTqq9omn4WHW0u7wEswKmZvbPVZeLidu9LESzAkieTiY+vWrTh27BgKCgq67KuurobBYMCAAQO8tg8aNAjV1dXdvl9zczMcDofXg8jtsK22y4jHtQQAVXYnDttqlUuKRLvr1WJZ40h5D288LGscESCx+Lhw4QIef/xxvPvuuzAajT2/QISCggKYzWbPIzk5WZb3pfBQU+e78AgkjpTV0Nwuaxwp75Kf4j+QOCJAYvFx9OhR1NTUYOzYsYiMjERkZCSKi4vx6quvIjIyEoMGDUJLSwuuXr3q9brLly/DYrF0+555eXmw2+2ex4ULFwI+GAo/iXHiilyxcaSs/lERssaR8pLM4j5bYuOIAInFx9SpU3Hy5EmUlZV5Ht/5zncwf/58z3/369cPe/fu9bymsrIS58+fR1ZWVrfvGRUVBZPJ5PUgcstMi4fVbISve9fq0LHqJTMtXsm0SKSP/+t2WeNIeb9/KFPWOCIAiJQSHBcXh4yMDK9t/fv3R0JCgmf74sWLsWrVKsTHx8NkMuGxxx5DVlYWbr31Vvmypj4jQq9Dfk46lm4+Bh3gNfHUXZDk56QjQu+rPKFQuiE+GoYInd9Jp4YIHW6Ij1YwK5LCHNMPqQnRfiedpiZEwxzTT8GsSOtk73C6bt06fO9738PcuXMxadIkWCwWbNu2Te5/hvqQmRlWrH9wLCydhnUtZiPWPziWfT5U7p8vzYIhovvikH0+tKH4ySlITei+QGSfDwpEwE3GgoVNxsiXdpeAw7Za1NQ5kRjXcamFIx7a8VVtE+56tRgNze3oHxWBj//rdo54aIy9sRUPbzyMS3YnksxG/P6hTI54kIeU728WH0RERNRrQe9wSkRERBQoFh9ERESkKBYfREREpCgWH0RERKQoSX0+tIwrJbTva0czvv/mQdQ2tCK+fz9s//FEXG+KCnVaJFJLmwubSs/iXG0jUuNjsCBrCAyR/PuHqC/qE6tdisqrsHpnhdcNyqxmI/Jz0tkjQiNGPb8bDmdbl+0mYyROPD8jBBmRFAW7KlBYYsO1Nx/W64Dc7DTkzUoPXWJEJBuudrlGUXkVlm4+1uXOqNV2J5ZuPoai8qoQZUZi+So8AMDhbMOo53crnBFJUbCrAm8d8C48AMAlAG8dsKFgV0VoEiOikAnr4qPdJWD1zgp0N7Tj3rZ6ZwXaO/9WJNX42tHss/Bwczjb8LWjWaGMSIqWNhcKS2x+YwpLbGhpcymUERGpQVgXH4dttV1GPK4lAKiyO3HYVqtcUiTJ9988KGscKWtT6dkuIx6duYSOOCLqO8K6+Kip8114BBJHyqttaJU1jpR1rrZR1jgiCg9hXXwkxhl7DpIQR8qL7y/uvhFi40hZqfExssYRUXgI6+IjMy0eVrMRvhbU6tCx6iUzLV7JtEiC7T+eKGscKWtB1hD0tKJdr+uII6K+I6yLjwi9Dvk5Hcv4Ov/+cz/Pz0lnvw8Vu94UBZPRfzsakzGS/T5UyhCpR252mt+Y3Ow09vsg6mPC/hM/M8OK9Q+OhcXsfWnFYjZi/YNj2edDA048P8NnAcI+H+qXNysdj0xK6zICotcBj0xinw+ivqhPNBkD2OE0HLDDqbaxwylReJPy/d1nig8iIiIKHnY4JSIiItXqMzeWI6LQ4qVPInJj8UFEQcebOxLRtXjZhYiCijd3JKLOWHwQUdDw5o5E1B1ediHNOPrlN5j7u796nv9lyW0YN/S6EGZEPZFyc8esYQnKJUYB4bwdkguLD9KEIc981GWbuxA5u2a20umQSLy5Y/jgvB2SEy+7kOp1V3hI2U+hw5s7hgfO2yG5sfggVTv65TeyxpGyhFZxcznExpHyOG+HgoHFB6natXM85IgjZT2w8XNZ40h5UubtEInF4oOIiHzivB0KBhYfRETkE+ftUDCw+CBV+8uS22SNI2VtffhWWeNIeZlp8bCajfC1oFaHjlUvmWnxSqZFGsfig1RNbB8P9vtQp1tvEte7Q2wcKS9Cr0N+TjoAdClA3M/zc9LZ74MkkVR8rF+/HqNGjYLJZILJZEJWVhY+/vhjz/7JkydDp9N5PR599FHZk6a+pac+HuzzoW48f9o3M8OKaemJXVa8CACmpSeyzwdJJqn4GDx4MNasWYOjR4/ib3/7G6ZMmYK7774bf//73z0xubm5qKqq8jxefvll2ZOmviX3nSO92k+hxT4t2lewqwJ7Kmq63benogYFuyoUzoi0TlLxkZOTg1mzZuFb3/oWbrrpJrz00kuIjY3FoUOHPDExMTGwWCyeh8lkkj1p6juaWtp9/tJz21NRg6aWdoUyIikqL9XJGkfKa2lzobDE5jemsMSGljaXQhlROAh4zkd7ezu2bt2KhoYGZGVleba/++67GDhwIDIyMpCXl4fGxka/79Pc3AyHw+H1IHL7pci/qMTGkbJmvXZA1jhS3qbSs+ipf5hL6IgjEkvyvV1OnjyJrKwsOJ1OxMbGYvv27UhP75iM9MADDyA1NRVJSUk4ceIEnn76aVRWVmLbtm0+36+goACrV68O/AgorJ294r94lRpHymoX2fRSbBwp71ytuM+W2DgiIIDiY/jw4SgrK4Pdbsf//M//YNGiRSguLkZ6ejqWLFniiRs5ciSsViumTp2KM2fOYNiwYd2+X15eHlatWuV57nA4kJycHMChUDgakhCDklPi4kh9InTiCosILpRQrdR4cZ8tsXFEQACXXQwGA2688UaMGzcOBQUFGD16NH7zm990GzthwgQAwOnTp32+X1RUlGf1jPtB5PbTWemyxpGydj02SdY4Ut6CrCHoaRWtXtcRRyRWr/t8uFwuNDc3d7uvrKwMAGC1chkWBSbaEIHp6Yl+Y6anJyLaEKFQRiTF8KQ4WeNIeYZIPXKz0/zG5GanwRDJtlEknqSflry8PBw4cABnz57FyZMnkZeXh/3792P+/Pk4c+YMXnjhBRw9ehRnz57Fjh07sHDhQkyaNAmjRo0KVv7UBxQuHN+r/RRa7POhfXk9jCz2tJ+oM0nFR01NDRYuXIjhw4dj6tSpOHLkCHbv3o3p06fDYDDgk08+wZ133okRI0bgiSeewNy5c7Fz585g5U59xNA8/30getpPocU+H9rHzyDJTdKE07ffftvnvuTkZBQXF/c6IaJr2WoaRC3zs9U0IC2xvzJJkWgnz9tFx41MMQc5GwoEP4MUDLxIR6o28zfiClqxcaSsOW8elDWOlMfPIAUDiw9StWaRDSDExpGyxJ4Vnj314meQgoHFB6lalMgGEGLjSFlizwrPnnrxM0jBwOKDVK3o8dtljSNl7fjxRFnjSHn8DFIwsPggVUtL7C+qwREnuqmT2EmknGyqXvwMUjCw+CDV+7Jgts9ffnpdx35SL/b50D5+BkluLD5IExL6GyRtJyJ5JcdHS9pO5A+LD1K98S/uwdf1Ld3u+7q+BeNf3KNwRiQFm4xp3+2v7MO5K03d7jt3pQm3v7JP4YxI61h8kKrV1rf4LDzcvq5vQW0PMRQaH35+QdY4Up69sdVn4eF27koT7I2tCmVE4YDFB6nafb/7q6xxpKzl20/IGkfKe3jjYVnjiAAWH6RyNXXiRjTExhGRNJfsTlnjiAAWH6RyiXHiJpSKjSMiaZLMRlnjiAAWH6RyW5fcJmscKev174+SNY6U9/uHMmWNIwJYfJDKxccacH2s/1GN62MNiO8hhkLjexOSZY0j5Zlj+iE1wf9y2tSEaJhj+imUEYUDFh+kekd+Ph3R/br/UY3up8eRn09XOCOSgk3GtK/4ySk+C5DUhGgUPzlF4YxI61h8kOoV7KpAU6ur231NrS4U7KpQOCOSoqi8yueN43T/3k/qV/zkFPzfc3diXMoAWM1GjEsZgP977k4WHhQQnSAIqroPssPhgNlsht1uh8lkCnU6FGItbS6MePZjuPz8lOp1wD9euAuGSNbSatPuEjBx7T5U+VgJoQNgMRtx8OkpiOjpBiJEpGpSvr/525pUbVPpWb+FBwC4hI44Up/DtlqfhQcACACq7E4cttUqlxQRhRyLD1K1c7WNssaRsmrqxPV+EBtHROGBxQepWmp8jKxxpKzEOHG9H8TGEVF4YPFBqvbAhFRZ40hZmWnxsJqNfiecWs1GZKbFK5kWEYUYiw9StbILV2WNI2VF6HXIz0n3G5Ofk87JphrR0ubC2yVf4rkPyvF2yZdoaet+FRpRTyJDnQCRP5wzoH0zM6yIM0bC4Wzrsi/OGImZGdYQZEVSFeyqQGGJzWsC+Eu7vkBudhryZvkvMIk648gHqRrnDGjf+Bf3dFt4AIDD2YbxL+5ROCOSqmBXBd46YOuy8swlAG8dsLHXDknG4oNU7dvJA2SNI2XV1rfg63r/dxz+ur4FtT3EUOi0tLlQWGLzG1NYYuMlGJKExQep2pbPz8kaR8q673d/lTWOlMdeOxQMLD5I1c78q17WOFJWTZ24EQ2xcaQ89tqhYGDxQar2taNZ1jhSVmKcuLsNi40j5bHXDgUDiw9StUEmcRNJxcaRsrYuuU3WOFLegqwh6GkltF7XEUckFosPUrW0gf1ljSNlxccacH2s/1GN62MNiO8hhkLHEKlHbnaa35jc7DTe2JEkkfTTsn79eowaNQomkwkmkwlZWVn4+OOPPfudTieWLVuGhIQExMbGYu7cubh8+bLsSVPfwb+6tO/Iz6f3aj+FXt6sdDwyKa3LZ1GvAx6ZxD4fJJ1OEIQe5jH/x86dOxEREYFvfetbEAQBf/jDH/DKK6/g+PHjuOWWW7B06VJ89NFH2LhxI8xmM5YvXw69Xo/PPvtMdEJSbslLfcOQZz7qMebsmtkKZEKB4PkLHy1tLmwqPYtztY1IjY/BgqwhHPEgDynf35I6nObk5Hg9f+mll7B+/XocOnQIgwcPxttvv40tW7ZgypQpAIANGzbg5ptvxqFDh3DrrbdKPAwi4GDF16LjJqZfH+RsSKqT5+2i40ammIOcDfWWIVKPxdlDQ50GhYGAS9b29nZs3boVDQ0NyMrKwtGjR9Ha2opp06Z5YkaMGIGUlBSUlpb6fJ/m5mY4HA6vB5Hbg+8cljWOlDXnzYOyxhFReJBcfJw8eRKxsbGIiorCo48+iu3btyM9PR3V1dUwGAwYMGCAV/ygQYNQXV3t8/0KCgpgNps9j+TkZMkHQUTqJPaaruhrv0QUFiQXH8OHD0dZWRk+//xzLF26FIsWLUJFReB9/fPy8mC32z2PCxcuBPxeRKQuYu9Vy3vaEvUtkosPg8GAG2+8EePGjUNBQQFGjx6N3/zmN7BYLGhpacHVq1e94i9fvgyLxeLz/aKiojyrZ9wPIrfNCzNljSNl7fjxRFnjiCg89HqassvlQnNzM8aNG4d+/fph7969nn2VlZU4f/48srKyevvPUB8ldhIpJ5uqk9hJpJxsStS3SCo+8vLycODAAZw9exYnT55EXl4e9u/fj/nz58NsNmPx4sVYtWoVPv30Uxw9ehQ//OEPkZWVxZUu1Cs9LcPkMk114/kjos4kLbWtqanBwoULUVVVBbPZjFGjRmH37t2YPr2jSdC6deug1+sxd+5cNDc3Y8aMGXjzzTeDkjj1LWfXzMbBiq+9VrVsXpjJEQ+NOLtmNk6et2POmwchoGOOx44fT+SIB1EfJanJmBLYZIx8YYMjIiL1ClqTMaJQKdhVgcISG1zXlMov7foCudls7UxEpDUsPkj1CnZV4K0Dti7bXQI821mAEBFpB8esSdVa2lwoLOlaeFyrsMSGljaXQhkREVFvsfggVdtUetbrUkt3XEJHHBERaQOLD1K1c7WNssYREVHocc4HqVpqfIyscRQ65//ViJm/KUZTqwvR/fQoevx2pAzkeSPqi7jUllStpc2FEc9+7PfSi14H/OOFu7jsVsVu/OlH6G5aTqQeOP1LNhkjCgdSvr/525pUzRCpR8YN/n+IM24wsfBQMV+FBwC0uTr2E1Hfwt/YpGotbS6Uf+XwG1P+lYOrXVTq/L8afRYebm2ujjgi6jtYfJCqcbWLts38TbGscUQUHlh8kKpxtYu2NbWKG5ESG0dE4YHFB6kaV7toW3Q/cb9ixMYRUXjgJ55Ubd74FFnjSFlFj98uaxwRhQcWH6Rq7x05L2scKStlYAx6WogUqQf7fRD1MSw+SNU450P7Tv9yts8ChH0+iPomdjglVeOcj/Bw+pez2eGUiDzY4ZRUjR1OiYi0gR1OKWwYIvXIzU7zG5ObncbCg4hIQ3jZhVQvb1Y6AKCwxOY1AqLXdRQe7v1ERKQNvOxCmtHS5sKm0rM4V9uI1PgYLMgawhEPIiKVkPL9zZEP0gxDpB6Ls4eGOg0iIuol/tlIREREimLxQURERIpi8UFERESK4pwP0gxOOCUiCg8sPkgTCnZVdFlq+9KuL7jUlohIg1h8kOoV7KrAWwdsXba7BHi2swAhItIOjlmTqrW0uVBY0rXwuFZhiQ0tbS6FMiIiot5i8UGqtqn0rN/7ugAdIyCbSs8qkg8REfUeiw9StXO1jbLGERFR6LH4IFVLjRd3y3WxcUREFHqSio+CggKMHz8ecXFxSExMxD333IPKykqvmMmTJ0On03k9Hn30UVmTpr5jQdYQ6HX+Y/S6jjgiItIGScVHcXExli1bhkOHDmHPnj1obW3FnXfeiYaGBq+43NxcVFVVeR4vv/yyrElT32GI1GPqzYl+Y6benMh+H0REGiJpqW1RUZHX840bNyIxMRFHjx7FpEmTPNtjYmJgsVjkyZD6tHaXgPKvHH5jyr9yoN0lIKKnIRIiIlKFXv25aLfbAQDx8fFe2999910MHDgQGRkZyMvLQ2Oj78mAzc3NcDgcXg8it8O2WlTZnX5jquxOHLbVKpQRERH1VsBNxlwuF1asWIHvfve7yMjI8Gx/4IEHkJqaiqSkJJw4cQJPP/00KisrsW3btm7fp6CgAKtXrw40DQpzNXX+Cw+pcUREFHoBFx/Lli1DeXk5Dh486LV9yZIlnv8eOXIkrFYrpk6dijNnzmDYsGFd3icvLw+rVq3yPHc4HEhOTg40LQoziXFGWeOIiCj0Aio+li9fjg8//BAHDhzA4MGD/cZOmDABAHD69Olui4+oqChERUUFkgb1AZlp8bCajai2O9FdrzEdAIvZiMy0+G72EhGRGkma8yEIApYvX47t27dj3759SEtL6/E1ZWVlAACr1RpQgtS3Reh1yM/xf9+W/Jx0TjYlItIQSSMfy5Ytw5YtW/DBBx8gLi4O1dXVAACz2Yzo6GicOXMGW7ZswaxZs5CQkIATJ05g5cqVmDRpEkaNGhWUA6DwNzPDipSEaJy70tRlX0pCNGZmsLAlItISnSAIPdw545pgXfd/XW7YsAEPPfQQLly4gAcffBDl5eVoaGhAcnIyvv/97+PnP/85TCaTqH/D4XDAbDbDbreLfg2Ftzmvl+DERd+roEYNNmHH8mwFMyIios6kfH9LGvnoqU5JTk5GcXGxlLck8qve2ea38ACAExcdqHe2IdYY8PxpIiJSENtCkqqtfO+4rHFERBR6LD5I1c5/03WeR2/iiIgo9Fh8kKqlXBctaxwREYUeiw9StXXzxsgaR0REocfig1Qt1hiJUYP9z5oeNdjEyaZERBrC4oNUb8fybJ8FCJfZEhFpD/9cJE3YsTwb9c42rHzvOM5/04SU66Kxbt4YjngQEWkQf3OTZsQaI1G4aHyo0yAiol7iZRciIiJSFIsPIiIiUhSLDyIiIlIUiw8iIiJSFIsPIiIiUhRXu5BmtLS5sKn0LM7VNiI1PgYLsobAEMn6mYhIa1h8kCYU7KpAYYkNLuE/217a9QVys9OQNys9dIkREZFkLD5I9Qp2VeCtA7Yu210CPNtZgBARaQfHrEnVWtpcKCzpWnhcq7DEhpY2l0IZERFRb7H4IFXbVHrW61JLd1xCRxwREWkDiw9StXO1jbLGERFR6LH4IFVLjY+RNY6IiEKPxQep2oKsIdDr/MfodR1xRESkDSw+SNUMkXrkZqf5jcnNTmO/DyIiDeFSW1I99zLazn0+9DqwzwcRkQbpBEHoYS2BshwOB8xmM+x2O0wmU6jTIRVhh1MiIvWS8v3NkQ/SDEOkHouzh4Y6DSIi6iUWH0SkiHpnG1a+dxznv2lCynXRWDdvDGKN/BWkJTyH2tfuEnDYVouaOicS44zITItHRE+z+oOAl12IKOjmvF6CExcdXbaPGmzCjuXZIciIpOI51L6i8iqs3lmBKrvTs81qNiI/Jx0zM6y9fn8p39+8YE5EQeXrSwsATlx0YM7rJQpnRFLxHGpfUXkVlm4+5lV4AEC13Ymlm4+hqLxK0XxYfBBR0NQ723x+abmduOhAvbNNoYxIKp5D7Wt3CVi9swLdXeZwb1u9swLtPd3LQkYsPkgzmlra8ez7J7Hg7c/x7Psn0dTSHuqUqAcr3zsuaxwpj+dQ+w7baruMeFxLAFBld+KwrVaxnCQVHwUFBRg/fjzi4uKQmJiIe+65B5WVlV4xTqcTy5YtQ0JCAmJjYzF37lxcvnxZ1qSp78l95whufq4Imw6dR8mpf2HTofO4+bki5L5zJNSpkR/nv2mSNY6Ux3OofTV1vguPQOLkIKn4KC4uxrJly3Do0CHs2bMHra2tuPPOO9HQ0OCJWblyJXbu3Ik///nPKC4uxqVLl3DvvffKnjj1HbnvHMGeippu9+2pqGEBomIp10XLGkfK4znUvsQ4o6xxcujVapevv/4aiYmJKC4uxqRJk2C323H99ddjy5Yt+H//7/8BAP7xj3/g5ptvRmlpKW699dYe35OrXehaTS3tuPm5oh7jvvjFTEQbIhTIiKSod7Yh4/ndPcaVPz+DSzZViudQ+9pdAiau3Ydqu7PbeR86ABazEQefntKrZbeKrXax2+0AgPj4eADA0aNH0draimnTpnliRowYgZSUFJSWlnb7Hs3NzXA4HF4PIrdf7qqQNY6UFWuMxKjB/n8JjRps4peWivEcal+EXof8nI7bUHQuLdzP83PSFe33EXDx4XK5sGLFCnz3u99FRkYGAKC6uhoGgwEDBgzwih00aBCqq6u7fZ+CggKYzWbPIzk5OdCUKAydvdIoaxwpb8fybJ9fXuwRoQ08h9o3M8OK9Q+OhcXsfWnFYjZi/YNjZenzIUXApeqyZctQXl6OgwcP9iqBvLw8rFq1yvPc4XCwACGPIQkxKDklLo7Ua8fybHbH1DieQ+2bmWHF9HSLKjqcBvRTs3z5cnz44Yc4cOAABg8e7NlusVjQ0tKCq1eveo1+XL58GRaLpdv3ioqKQlRUVCBpUB/w01np2HTovKg4UrdYYyQKF40PdRrUCzyH2heh1yFrWEKo05B22UUQBCxfvhzbt2/Hvn37kJaW5rV/3Lhx6NevH/bu3evZVllZifPnzyMrK0uejKlPiTZEYHp6ot+Y6emJnGxKRKQhkkY+li1bhi1btuCDDz5AXFycZx6H2WxGdHQ0zGYzFi9ejFWrViE+Ph4mkwmPPfYYsrKyRK10IepO4cLxPpfbTk9PROFC/iWmBfbGVjy88TAu2Z1IMhvx+4cyYY7pF+q0SAK13JSMtE9S8bF+/XoAwOTJk722b9iwAQ899BAAYN26ddDr9Zg7dy6am5sxY8YMvPnmm7IkS33XPy/XSdpO6nL7K/tw7sp/mlBV2Z0Y/Yv/RWpCNIqfnBLCzEisYN+UjPoW3tWWVK/zF1dn/AJTN54/7XPflKzzl4V7zCMUqyVIfXhXWwob9sZWv19cAHDuShPsja0KZURS8PxpnxpvSkbax+KDVO3hjYdljSNl8fxpnxpvSkbax+KDVO2Sn196gcSRsnj+tE+NNyUj7eszxUe7S0DpmSv4oOwrlJ65wiFCjUgyi7vRkdg4UhbPn/ap8aZkpH19ojUdZ2lr1+8fysToX/yvqDhSH54/7ctMi4fVbOzxpmSZafFKp0YaFvYjH+5Z2p2vWVbbnVi6+RiKyqtClBmJYY7p1+VGSJ3p/h1H6mOO6YfUBP+3Wk9NiOb5UzE13pSMtC+siw/O0ta+rx3N3Z6/awn/jiN1Kn5yis8ChMtstUFtNyUj7QvrPh+lZ67g/sJDPcb9MfdWVfS6p64mrtmLi1d7nsg2eIARB5+ZqkBGFCh2ONU+djglf6R8f4f1nA/O0ta+2gZx/R/ExlHomGP64S8//m6o06BeUMtNyUj7wvqyC2dpa198f3F/GYuNIyKi0Avr4sM9S9vXoKAOHateOEtbvbb/eKKscUREFHphXXxcO0vbF87SVrfrTVEwGf1fHTQZI3G9KUqhjIiIqLfCuvgAOmZpL5mUhs71hV4HLJmUxlnaGnDi+RmI9PGTGqnv2E9ERNoR9sVHUXkVfnfAhs6raQUB+N0BG/t8aEDuO0fQ5up+X5urYz+pX1NLO559/yQWvP05nn3/JJpa2kOdEknU0ubC2yVf4rkPyvF2yZdo8fXBJOpBWC+1bXcJmLh2n8+bIrk78x18egovvahUU0s7bn6uqMe4L34xE9GGCAUyokDkvnMEeypqumyfnp6IwoXjQ5ARSVWwqwKFJd5/yOl1QG52GvJm+b+8TX2DlO/vsB754N0Yte+XuypkjSPl+So8AGBPRQ1HrjSgYFcF3upmBNklAG8dsKGAnz+SKKyLD/b50L6zVxpljSNlNbW0+yw83PZU1PASjIq1tLlQWGLzG1NYYuMlGJIkrIsP9vnQviEJMbLGkbI4cqV9m0rPdhnx6MwldMQRiRXWxQf7fGjfT0VeSxYbR8riyJX2nasVd27ExhEBYV588G6M2hdtiMD09ES/MdPTEznZVKU4cqV9qfHizo3YOCIgzIsPgHdjDAfFlV/3aj+FDkeutG9B1pAufZI60+s64ojECusby7nNzLBierqFd2PUoK9qm9DS7v+Cc0u7gK9qm3BDfPe3bafQsTeKu+GfvbGVo1cqZYjUIzc7DW8d8D3pNDc7DQZfnQCJutFnflrcd2O8+9s3IGtYAgsPjbjr1WJZ40hZ33vtgKxxFBp5s9LxiI9O0Y9MYp8Pkq5PjHyQdjU0i1uCKTaOlGV3tskaR6GTNysdT9w5AptKz+JcbSNS42OwIGsIRzwoICw+SNX6R0XA4ey5sOgfxSF7NRL7tcSvL20wROqxOHtoqNOgMMDPPKnaBz/OljWOlPWt68QVhWLjiCg8cOSDVG1f5WXRcYsT+ReZ2lTWirucIjaOQut0dT3uerUYrS6gnx74+L9ux42W2FCnRRLUO9uw8r3jOP9NE1Kui8a6eWMQa1S+FGDxQarGJlXa1lNnTKlxFDppz3yEa09TqwuY9uti6ADY1swOVVokwZzXS3DiosPzvLK6DhnP78aowSbsWK7s6DEvu5DKif1W4reXGomdi8M5O+rWufC4lvDv/aRunQuPa5246MCc10sUzYfFB6laujVO1jhS1q/mjJY1jpR3urq+x9Je+HccqVO9s81n4eF24qID9QquOpNcfBw4cAA5OTlISkqCTqfD+++/77X/oYcegk6n83rMnDlTrnypj9n3D3HdS8XGkbJ+9KdjssaR8thrR/tWvndc1jg5SC4+GhoaMHr0aLzxxhs+Y2bOnImqqirP449//GOvkqS+q6lV3G26xcYRkTRiP1r8CKrX+W+aZI2Tg+QJp3fddRfuuusuvzFRUVGwWCwBJ0XkljYwBgdPi4sjIvn104srLPrxIr5qpVwXjcrqOlFxSgnKj8v+/fuRmJiI4cOHY+nSpbhy5Uow/hnqA3hjMm37y5LbZI0j5X38X7fLGkfKWzdvjKxxcpC9+Jg5cybeeecd7N27F2vXrkVxcTHuuusutLd336WyubkZDofD60HkFm2IwPT0RL8x09MTeVMylWoRxI3Fi40j5d1oiUVPd8LS/TuO1CnWGIlRg01+Y0YNNina70P24uO+++7DnDlzMHLkSNxzzz348MMPceTIEezfv7/b+IKCApjNZs8jOTlZ7pRI4y47nL3aT6FTUyfu3IiNo9CwrZntswBhnw9t2LE822cBEoo+H0Evc4YOHYqBAwfi9OnTmDp1apf9eXl5WLVqlee5w+FgAUIeUpaIhaJLH/mXGGeUNY5Cx7ZmNjucatyO5dmq6XAa9ClCFy9exJUrV2C1WrvdHxUVBZPJ5PUgclPjEjESLzMtHlaz0e9fzVazEZlp8UqmRQGKNUbCHN0PhggdzNH9WPBrUIReB4vZiMS4KFjMRkToe7qoFhySf3Lq6+tx+vR/lh/YbDaUlZUhPj4e8fHxWL16NebOnQuLxYIzZ87gqaeewo033ogZM2bImjj1DWpcIkbiReh1yM9Jx6Obu+/jIQDIz0kP2S9AEu/mZz/2WtL+r4ZW3LpmL6L76fHFC/5XQJI65L5zBHsqajzPS04Bmw6dx/T0RBQuHK9oLpJHPv72t79hzJgxGDOmY1bsqlWrMGbMGDz33HOIiIjAiRMnMGfOHNx0001YvHgxxo0bh5KSEkRFRcmePIU/sUu/lFwiRtIs3+K/gVhP+yn0Ohce12pqdeHmZz9WOCOSqnPhca09FTXIfeeIovlIHvmYPHkyBMF3s93du3f3KiGia62dOxp7XtwjKo7U5/y/GtHWw0KWNldHXAp7tahS9VVnj038mlpdqL7qhGUA5+6oUVNLu8/Cw21PRQ2aWtoVWznItjCkatuPX5Q1jpQ18zfiWm6LjSPlfe+1A7LGkfJ+uatC1jg5sPggVTtX2yhrHCmL7fG1zyHyZmNi40h5Z6+I+/0oNk4OLD5I1VLjxQ3Fi40jZUWL7LktNo6UZxK5okVsHClvSIK4349i4+TATzyp2rzxKbLGkbL+55HvyhpHyvvwsUmyxpHy1HibChYfpGrvHTkvaxwpa93eSlnjSHmWAb77tLjp/h1H6qTG21Sw+CBV45wPbWOfFu2rd7bB9/rGDsK/40i9CheO91mAhKLPBy/Skapxzoe2qfFW3iSNlC7DhYuU/QIjaQoXjkdTSzt+uasCZ680YkhCDH46Kz0kN+Zk8UGqtiBrCF7a9QVcfv700us64kh91s0bg4zne+79o+StvEkajl6Fl2hDBF64Z2So0+BlF1I3Q6QeudlpfmNys9NgiOSPshqp8VbeJA27DFMw8Dc2qV7erHQ8MikNnW//odcBj0xKQ56CM7RJOrXdypukETsqxdErkoJ/bpAm5M1KxxN3jsCm0rM4V9uI1PgYLMgawhEPjVDTrbxJGvfo1YmLDp8xHL0iqXSCvxu1hIDD4YDZbIbdbofJ5H+4loiIlDHn9ZJuCxCOXpGblO9vlqpERNQjjl6RnPhTQ0REosQaI7mclmTBC+ZERESkKI58EJEi2l0CDttqUVPnRGKcEZlp8YjovISJiPoEFh9EFHRF5VVYvbMCVXanZ5vVbER+TjpmZlhDmBkRhQIvuxBRUBWVV2Hp5mNehQcAVNudWLr5GIrKq0KUGRGFCosPIgqadpeA1Tsrur0xmXvb6p0VaPfXP5+Iwg6LDyIKmsO22i4jHtcSAFTZnThsq1UuKSIKORYfRBQ0NXW+C49A4ogoPLD4IKKgSYwzyhpHROGBq12IKGgy0+JhNRtRbXd2O+9DB8Bi7lh2S+rH5dIkFxYfRBQ0EXod8nPS8ejmY93uFwDk56TzC0wDuFya5MTLLkQUVG/uP92r/RR6XC5NcmPxQURBU+9s83srdgA4cdGBemebQhmRVFwuTcHA4oOIgmble8dljSPlcbk0BQOLDyIKmvPfNMkaR8rjcmkKBhYfRBQ0KddFyxpHyuNyaQoGFh9EFDTr5o2RNY6U514u7Ws9kg4dq164XJqkYPFBREETa4zEqMEmvzGjBpsQa+Sqf7VyL5cG0KUAcT/ncmmSSnLxceDAAeTk5CApKQk6nQ7vv/++135BEPDcc8/BarUiOjoa06ZNw6lTp+TKl4g0ZsfybJ8FyKjBJuxYnq1wRiTVzAwr1j84Fhaz96UVi9mI9Q+OZZ8PkkzynxsNDQ0YPXo0Hn74Ydx7771d9r/88st49dVX8Yc//AFpaWl49tlnMWPGDFRUVMBo5DVBor5ox/Js1DvbsPK94zj/TRNSrovGunljOOKhITMzrJiebmGHU5KFThCEgBdn63Q6bN++Hffccw+AjlGPpKQkPPHEE/jJT34CALDb7Rg0aBA2btyI++67r8f3dDgcMJvNsNvtMJn8D9cSERGROkj5/pZ1zofNZkN1dTWmTZvm2WY2mzFhwgSUlpZ2+5rm5mY4HA6vBxEREYUvWYuP6upqAMCgQYO8tg8aNMizr7OCggKYzWbPIzk5Wc6UiIiISGVCvtolLy8Pdrvd87hw4UKoUyIiIqIgkrX4sFgsAIDLly97bb98+bJnX2dRUVEwmUxeDyIiIgpfshYfaWlpsFgs2Lt3r2ebw+HA559/jqysLDn/KSIiItIoyevc6uvrcfr0f26BbbPZUFZWhvj4eKSkpGDFihV48cUX8a1vfcuz1DYpKcmzIoaIiIj6NsnFx9/+9jfccccdnuerVq0CACxatAgbN27EU089hYaGBixZsgRXr17FxIkTUVRUxB4fREREBKCXfT6CgX0+iIiItCdkfT6IiIiIeqK63sbugRg2GyMiItIO9/e2mAsqqis+6urqAIDNxoiIiDSorq4OZrPZb4zq5ny4XC5cunQJcXFx0OnkvWGRw+FAcnIyLly4EJbzScL9+IDwP0Yen/aF+zHy+LQvWMcoCALq6uqQlJQEvd7/rA7VjXzo9XoMHjw4qP9GuDczC/fjA8L/GHl82hfux8jj075gHGNPIx5unHBKREREimLxQURERIrqU8VHVFQU8vPzERUVFepUgiLcjw8I/2Pk8WlfuB8jj0/71HCMqptwSkREROGtT418EBERUeix+CAiIiJFsfggIiIiRbH4ICIiIkWFTfHR3t6OZ599FmlpaYiOjsawYcPwwgsv9Nhjfv/+/Rg7diyioqJw4403YuPGjcokLFEgx7d//37odLouj+rqagUzF6+urg4rVqxAamoqoqOjcdttt+HIkSN+X6OV8+cm9RjVfg4PHDiAnJwcJCUlQafT4f333/faLwgCnnvuOVitVkRHR2PatGk4depUj+/7xhtvYMiQITAajZgwYQIOHz4cpCPwLxjH9/zzz3c5nyNGjAjiUfjW0/Ft27YNd955JxISEqDT6VBWVibqff/85z9jxIgRMBqNGDlyJHbt2iV/8iIF4xg3btzY5RwajcbgHEAP/B1fa2srnn76aYwcORL9+/dHUlISFi5ciEuXLvX4vsH+DIZN8bF27VqsX78er7/+Or744gusXbsWL7/8Ml577TWfr7HZbJg9ezbuuOMOlJWVYcWKFfjRj36E3bt3K5i5OIEcn1tlZSWqqqo8j8TERAUylu5HP/oR9uzZg02bNuHkyZO48847MW3aNHz11Vfdxmvp/LlJPUY3tZ7DhoYGjB49Gm+88Ua3+19++WW8+uqr+O1vf4vPP/8c/fv3x4wZM+B0On2+53vvvYdVq1YhPz8fx44dw+jRozFjxgzU1NQE6zB8CsbxAcAtt9zidT4PHjwYjPR71NPxNTQ0YOLEiVi7dq3o9/zrX/+K+++/H4sXL8bx48dxzz334J577kF5eblcaUsSjGMEOrqDXnsOz507J0e6kvk7vsbGRhw7dgzPPvssjh07hm3btqGyshJz5szx+56KfAaFMDF79mzh4Ycf9tp27733CvPnz/f5mqeeekq45ZZbvLbNmzdPmDFjRlBy7I1Aju/TTz8VAAjffPNNkLPrvcbGRiEiIkL48MMPvbaPHTtW+NnPftbta7R0/gQhsGPU0jkEIGzfvt3z3OVyCRaLRXjllVc8265evSpERUUJf/zjH32+T2ZmprBs2TLP8/b2diEpKUkoKCgISt5iyXV8+fn5wujRo4OYaWA6H9+1bDabAEA4fvx4j+/zgx/8QJg9e7bXtgkTJgiPPPKIDFn2jlzHuGHDBsFsNsuamxz8HZ/b4cOHBQDCuXPnfMYo8RkMm5GP2267DXv37sU///lPAMD//d//4eDBg7jrrrt8vqa0tBTTpk3z2jZjxgyUlpYGNddABHJ8bt/+9rdhtVoxffp0fPbZZ8FONSBtbW1ob2/vMnQZHR3t869CLZ0/ILBjdNPCOezMZrOhurra6xyZzWZMmDDB5zlqaWnB0aNHvV6j1+sxbdo01Z3XQI7P7dSpU0hKSsLQoUMxf/58nD9/PtjpKkZrn8tA1dfXIzU1FcnJybj77rvx97//PdQpiWK326HT6TBgwIBu9yv1GQyb4uOZZ57BfffdhxEjRqBfv34YM2YMVqxYgfnz5/t8TXV1NQYNGuS1bdCgQXA4HGhqagp2ypIEcnxWqxW//e1v8Ze//AV/+ctfkJycjMmTJ+PYsWMKZi5OXFwcsrKy8MILL+DSpUtob2/H5s2bUVpaiqqqqm5fo6XzBwR2jFo6h52556V0d458zVn517/+hfb2dkmvCZVAjg8AJkyYgI0bN6KoqAjr16+HzWZDdnY26urqgpqvUnx9LtV2/npj+PDh+P3vf48PPvgAmzdvhsvlwm233YaLFy+GOjW/nE4nnn76adx///0+byin1GdQdXe1DdSf/vQnvPvuu9iyZQtuueUWzxyApKQkLFq0KNTp9Vogxzd8+HAMHz7c8/y2227DmTNnsG7dOmzatEmp1EXbtGkTHn74Ydxwww2IiIjA2LFjcf/99+Po0aOhTk02Uo9Ra+eQenbtaOWoUaMwYcIEpKam4k9/+hMWL14cwsxIrKysLGRlZXme33bbbbj55pvx1ltv4YUXXghhZr61trbiBz/4AQRBwPr160OdTviMfDz55JOe0YGRI0diwYIFWLlyJQoKCny+xmKx4PLly17bLl++DJPJhOjo6GCnLEkgx9edzMxMnD59OkhZ9s6wYcNQXFyM+vp6XLhwAYcPH0ZrayuGDh3abbyWzp+b1GPsjprP4bUsFgsAdHuO3Ps6GzhwICIiIiS9JlQCOb7uDBgwADfddJMmzqkYvj6Xajt/cnKPRqv1HLoLj3PnzmHPnj0+Rz0A5T6DYVN8NDY2Qq/3PpyIiAi4XC6fr8nKysLevXu9tu3Zs8erolWLQI6vO2VlZbBarXKmJrv+/fvDarXim2++we7du3H33Xd3G6el89eZ2GPsjhbOIQCkpaXBYrF4nSOHw4HPP//c5zkyGAwYN26c12tcLhf27t2ruvMayPF1p76+HmfOnNHEORVDy5/LQLW3t+PkyZOqPIfuwuPUqVP45JNPkJCQ4Ddesc+gbFNXQ2zRokXCDTfcIHz44YeCzWYTtm3bJgwcOFB46qmnPDHPPPOMsGDBAs/zL7/8UoiJiRGefPJJ4YsvvhDeeOMNISIiQigqKgrFIfgVyPGtW7dOeP/994VTp04JJ0+eFB5//HFBr9cLn3zySSgOoUdFRUXCxx9/LHz55ZfC//7v/wqjR48WJkyYILS0tAiCoO3z5yb1GNV+Duvq6oTjx48Lx48fFwAIv/rVr4Tjx497ZtKvWbNGGDBggPDBBx8IJ06cEO6++24hLS1NaGpq8rzHlClThNdee83zfOvWrUJUVJSwceNGoaKiQliyZIkwYMAAobq6OiyO74knnhD2798v2Gw24bPPPhOmTZsmDBw4UKipqVHd8V25ckU4fvy48NFHHwkAhK1btwrHjx8XqqqqPO+xYMEC4ZlnnvE8/+yzz4TIyEjhv//7v4UvvvhCyM/PF/r16yecPHlS8eMThOAc4+rVq4Xdu3cLZ86cEY4ePSrcd999gtFoFP7+97+r6vhaWlqEOXPmCIMHDxbKysqEqqoqz6O5udnzHqH4DIZN8eFwOITHH39cSElJEYxGozB06FDhZz/7mdf/4EWLFgm333671+s+/fRT4dvf/rZgMBiEoUOHChs2bFA2cZECOb61a9cKw4YNE4xGoxAfHy9MnjxZ2LdvXwiyF+e9994Thg4dKhgMBsFisQjLli0Trl696tmv5fPnJvUY1X4O3UuBOz8WLVokCELHctRnn31WGDRokBAVFSVMnTpVqKys9HqP1NRUIT8/32vba6+9JqSkpAgGg0HIzMwUDh06pNAReQvG8c2bN0+wWq2CwWAQbrjhBmHevHnC6dOnFTyq/+jp+DZs2NDt/muP5/bbb/fEu/3pT38SbrrpJsFgMAi33HKL8NFHHyl3UJ0E4xhXrFjh+fkcNGiQMGvWLOHYsWPKHti/+Ts+9/Lh7h6ffvqp5z1C8RnUCUIPLUCJiIiIZBQ2cz6IiIhIG1h8EBERkaJYfBAREZGiWHwQERGRolh8EBERkaJYfBAREZGiWHwQERGRolh8EBERkaJYfBAREZGiWHwQERGRolh8EBERkaJYfBAREZGi/j+ktWVCcDC6BgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "plt.scatter('Month','Price',data=new_pumpkins)" @@ -81,13 +429,1358 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "\n", "plt.scatter('DayOfYear','Price',data=new_pumpkins)" ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\billg\\AppData\\Local\\Temp\\ipykernel_5488\\3995499251.py:1: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", + " day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n" + ] + } + ], + "source": [ + "day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.14878293554077535\n", + "-0.16673322492745407\n" + ] + } + ], + "source": [ + "print(new_pumpkins['Month'].corr(new_pumpkins['Price']))\n", + "print(new_pumpkins['DayOfYear'].corr(new_pumpkins['Price']))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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zQHm542JWVChxKyocF5PI3bCNDJEkHL/GmLn2QmvWqBu3ogL4z3+AGzdurmvZEujVC/D1VSfmjRvAyZPGSVNgINC9uxKbiGzHOzJEknD8GmPm2gvNn69u3PpJDKC8/uGH5h97ypQp0Gg0DZa9ewtQXg6kpS1HfLwX3n//FZSXK8lNrfT0dAQHBxu9rn1/ixYtEBERgUceeQSnTp2CEAJJSUkmZ9V+6623EBwcjKSkJJNlqV26detmeM/y5cvh5eWFV155pcHx6pfL1jqnpKQ05RQSWcVEhkgijl+jsNRe6IsvgKoqdeLq9Q2TmFo3btjnMVNKSgqKi4sNS1FRMQIDowAAn366GpMmLcTWrasBKPEsPWYKDAxEcXEx/vvf/+Ljjz/GiRMn8Jvf/AYajQZpaWnIycnBO++8Y9i/qKgICxcuxN///nd8/PHHRuUAgLS0NMPrw4cPG963evVqLFy4EKtXr7ZLnYuLi7He077U5DBMZIgaKTMTWLYM2LWr+ceqHb9GpwO2b1f+m5HheV2vrbUXMpdsNJauVIcd+TuQX6o0wLlyxfL+P//c/Ji+vr4IDw83LG3bhsPLywtHj+5HZeU1zJy5DFeulOPbbw8CACorzR9Lo9EgPDwcERERGDx4MKZNm4avvvoK5eXliIyMxN/+9jcsWLAARUVFEEJg2rRpGD58OCZNmoSgoCCjcgBAcHCw4XX79u0BAPv378e1a9ewbNkylJeX4+DBg82us1JvD/tSk8PwaSyRjQoLgfh4oLT05rrQUODwYSAqqnnHljVWj7Ow1l6oue1Gyq6VYfzH45FZeLMBjraHFu8krwdg/gLr79+8uKbUtrvZujUVw4ePQ8uW3hg+fBy2bk1F//6DbW6Xc/78eWzevBleXl7w+uX55OTJk7F582ZMnToVY8aMQV5eHr7//vtGlS81NRXjxo2Dt7c3xo0bh9TUVAwePLhRxyByJN6RIbJR/SQGUF4PGiSnPO7EUnuhu+4CvL2bd/zxH49H1knjBjhZJ7MwY9c4s0lSy5ZKA9zm2rZtG/z9/Q3LpEm/QYsW5di9+yOMGDERADBixERkZX0IL6+f0aqV+WPp9Xr4+/ujTZs26NChA/bu3YvZs2ejTZs2hn3effdd5OXlYe7cuXj33XcNd1psUV5ejo8++ggTJyrlmjhxIj788EP83MhbU/Xr7O/vj5dffrlRxyCyFRMZIhtkZjZMYmqVltrnMZOnM9de6LXXmndcXakOmYWZqBbGDXCqRTUyCzPh3SG/QTJT22vJHu69917k5uYaljfeeAOHDq1H1649EBvbHwAQFzcAnTp1xeHD/7Z4rICAAOTm5uLIkSN47bXXcNttt+Gll14y2icsLAwzZsxAr169MGrUqEaVdf369ejRowf691fKNWDAAHTt2hX//rflctVXv865ubmYOXNmo45BZCs+WiKyQU6O5e3Z2UBysmPK4q7MzXdVUaF0zW6qwjLLDXBO/VyAEQNiUF6utInx97fPnZhabdq0QXS9vvTp6anIz/8ed9xx809wTU0N1qxZjenTp5k9VosWLQzH6tWrFwoLCzFr1iy8//77Rvu1bNkSLZvwPC41NRXff/+90XtramqwevVqTJtmvlz1maozkVqYyBDZID7e8vaEBMeUwxPYu71QjxDLDXCiQ5QLbmCgfRMYc44dO4YjR45g3759CAkJMawvKyvD0KFD8Z///Ac9e/a06ViLFi1Cjx49MG/ePNx2221OUy4iR2IiQ2QDrVZp2Gvq8VJoKO/GOLPY0Fhoe2iRdTLL6PGSl8YLSd2TEBPq2FbWqamp+NWvfoUhQ4Y02DZo0CCkpqaaHL/FlMjISIwePRpLly7Ftm3bHFau6upq5ObmGu3j6+uLXr88j6usrERJSYnR9pYtW6Jdu3bNKiORKWwjQ2Sjw4eVpKWu2l5L5NzWj12PpO7GDXCSuidh/VjHjm1y/fp1/Otf/8LYsWNNbh87dizWrl2LqkYMnDNv3jx89tln+OqrrxxWrp9//hm33nqr0fLAAw8Y9s/IyEBERITRctdddzW5fESWaIQQQnYh1FReXo6goCDo9XoEOuK+Mbm9XbuUNjEJCbwT4wgVFRUoKipCVFQUWlnq0mOD/NJ8FJQVIDok2uF3YoioIUu/b1uv305zR2bFihXQaDSYO3euYd3QoUMbDHPNlu8kW3IysHQpkxhXFBMagxExIzw+ieFkleROnKKNzOHDh/HOO++gX79+Dbb9/ve/x7Jlywyv/fz8HFk0IiK3wckqyR1JvyPz888/Y8KECfjnP/9pcghrPz8/o2Gu+XiIiKhp6icxABpMVknkaqQnMrNnz8bIkSORVH8krF988MEHaNeuHfr06YPFixfj6tWrFo9XWVmJ8vJyo4WIyNNVVJifBNPaZJVEzkzqzcQNGzbg66+/Npp1ta7x48eja9eu6NixI7777js888wzOHHiBDZt2mT2mMuXL8cLL7ygVpGJiFySpckoa7c3sy01kRTSEpnTp0/jySefxK5du8z2RJg+fbrh//v27YuIiAgkJiaisLAQPczMMrd48WI89dRThte1s8ISEXkya5NR2jpZJZGzkZbIHD16FOfPnzcajbK6uhqff/45/vGPf6CystIwo2ut+F+GVy0oKDCbyPj6+sKXv0giIiOtWikNe009XgoM5N0Ycl3SEpnExEQcO3bMaN1jjz2Gnj174plnnmmQxAAwjCQZERHhiCISEbmV7t3N91oiclXSEpmAgAD06dPHaF2bNm0QGhqKPn36oLCwEOvWrcN9992H0NBQfPfdd5g3bx6GDBlisps2ERFZ1rIlEBurNOytrFQeJ/FODLk6px05wMfHB1lZWVi5ciWuXLmCyMhIjB07Fs8++6zsohERubRWrZjAkPuQ3v26rn379mHlypUAlMnQ9u/fj9LSUlRUVCA/Px9/+ctfOI4MEbmEKVOmmB2NfPbs2dBoNJgyZYrR/qNGjQKgtBccPHgwxowZY/Q+vV6PyMhI/N///R8A4Mcff2ww+nntcujQIQBAenq6YZ2Xlxfatm2L+Ph4LFu2DHq93mId9u3bB41Gg0uXLjXY1q1bN8Pf69rXpsqxYsUKo7LWNhGoX/aQkBDcc889OHDggFGc559/HgMGDDBbxurqarz++uvo27cvWrVqhbZt22LEiBH48ssvG+x77do1PPfcc4iNjYWvry/atWuH3/zmN/j+++8bxNRoNEhJSWlwjFdeeQUajQZDhw41WZ6jR48anf/6EhMTjT7XM2fOwMfHp8ETilp1z1FgYCAGDRqETz75xGif9PR0BAcHG7029VnUdqwx952pXZ5//nmrn5ep71p1dTVWrFiBnj17onXr1ggJCUF8fDzee+89k3WzF6dKZIiI3ElkZCQ2bNiAa9euGdZVVFRg3bp16NKli9n3eXl5IT09HRkZGfjggw8M6+fMmYOQkBA899xzRvtnZWWhuLjYaBk4cKBhe2BgIIqLi3HmzBkcPHgQ06dPx9q1azFgwACcPXvWbvVdtmxZg3LMmTPH4ntqy/7555+jY8eOuP/++3Hu3Dmb4gkh8Oijj2LZsmV48skn8cMPP2Dfvn2IjIzE0KFDsWXLFsO+lZWVSEpKwurVq/Hiiy9Cp9Nh+/btuHHjBuLj4xskHhEREdi7dy/OnDljtH716tUWP7uBAweif//+WL16dYNtP/74I/bu3Ytp06YZ1qWnp+Phhx9GeXk5cnJyTB4zLS0NxcXFOHLkCO6880489NBDDdqY1lf7mdddfvrpJwAwWrdy5coG+y5YsMDscS1911544QW8/vrr+NOf/oTjx49j7969mD59uslE2J6c9tESEZH96QAUAogGoP58S7fddhsKCwuxadMmTJgwAQCwadMmdOnSBVFRURbfGxsbixUrVmDOnDkYNmwYvvrqK2zYsAGHDx+Gj4+P0b6hoaEIDw83eyyNRmPYHhERgV69euGBBx5A7969sXDhQvzrX/9qZk0VAQEBFsthSm3Zw8PDsWTJEmzYsAE5OTn49a9/bfW9H374IT766CNs3brVaPbtd999F6Wlpfjd736H5ORktGnTBitXrkR2dja++eYb9O/fHwDQtWtXfPzxx4iPj8e0adOQl5cHjUYDAAgLC8PAgQOxZs0awx2wgwcP4sKFC/jNb36D48ePmy3XtGnT8Oyzz2LlypVG0+qkp6cjIiLCcKdHCIG0tDS89dZb6Ny5M1JTUw29c+sKDg42nKM//elP+Nvf/oa9e/eib9++ZstQ9zOvr+76oKAgk/teuHDB5Hstfde2bt2Kxx9/HL/5zW8M62rPtZp4R4aIPEAZgBQAcQDuAxD7y+uLqkeeOnUq0tLSDK9Xr16Nxx57zKb3zpkzB/3798ekSZMwffp0LF261G4XhrCwMEyYMAFbt25FdXW1XY7ZHNeuXcPatWsBoEGiZs66desQGxtrlMTUmj9/PkpLS7Fr1y7DvsnJyQ3OX4sWLTBv3jwcP34c3377rdG2qVOnIj093fB69erVmDBhgtXyTZgwAZWVlfjoo48M64QQWLNmDaZMmWLolbt3715cvXoVSUlJmDhxIjZs2IArV66YPe6NGzeQmpoKwPZz5Ejh4eHYs2cP/ve//zk0LhMZIvIA4wFk1VuXBWCc6pEnTpyIL774Aj/99BN++uknfPnll5g4caJN79VoNFi1ahV2796NDh06YNGiRSb3Gzx4MPz9/Y0WW/Ts2ROXL19GaWmpxf06d+7c4PinTp1qsN8zzzzTYL/6bV7Mlb1NmzZ49dVXMXDgQCQmJtpUfp1Oh169epncVrtep9M1et9a999/P8rLy/H555/jypUr+PDDDzF16lSr5QoJCcHo0aONHi/t3bsXP/74o1ESm5qaikcffRReXl7o06cPunfvjo0bNzY43rhx4+Dv7w9fX1/MmzcP3bp1w8MPP2yxDHq9vsFnMWLECKtlt8bSd+2vf/0r/ve//yE8PBz9+vXDzJkzsWPHjmbHtIaPlojIzekAZJpYX/3L+nyo+Zipffv2GDlyJNLT0yGEwMiRI9GuXTub37969Wr4+fmhqKgIZ86cQbdu3Rrs8+9//9vsRdoSIQQAGB6nmHPgwAEEBAQYrTPV2PXpp582asAMAJ06dbJ47H//+9/o2bMn8vLysHDhQqSnp8Pb29t64X9RWwd77wsA3t7emDhxItLS0nDy5EnExsbaPPzH1KlTodVqDSPRr169Gvfccw+io6MBAJcuXcKmTZvwxRdfGN4zceJEpKamNjiHr7/+OpKSknDy5EnMmzcPb7zxBkJCQizGDwgIwNdff220rnXr1jaV3RJL37VbbrkFeXl5OHr0KL788kt8/vnneOCBBzBlyhRVG/wykSEiN1doZXsB1G4vM3XqVDzxxBMAgDfffNPm9x08eBCvv/46du7ciRdffBHTpk1DVlZWg8QjMjLScIFsjB9++AGBgYEIDQ21uF9UVJRRrxgAaNmy4eWjXbt2jS5HZGQkYmJiEBMTgxs3bmD06NHIy8uzaYT22NhY/PDDDya31a6PjY1t9L51TZ06FfHx8cjLy7PpbkytxMREdOnSBenp6Xj66aexadMmvPPOO4bt69atQ0VFhVGbGCEEampqoNPpjMoSHh6O6OhoREdHIy0tDffddx+OHz+OsLAws/FbtGjRpO+ENda+ay1atMCgQYMwaNAgzJ07F//6178wadIk/N///Z/VdmFNxUdLROTmTE9ncpP9/9jXl5KSguvXr6Oqqgpardam91y9ehVTpkzBrFmzcO+99yI1NRVfffUV3n77bbuU6fz581i3bh1GjRqFFi2c41Lw0EMPoWXLlnjrrbds2v/RRx9Ffn4+Pv300wbbXnvtNYSGhiI5Odmwb1ZWVoN2MDU1NXj99ddxyy23mGx/1Lt3b/Tu3Rt5eXkYP368zXVp0aIFHnvsMaxZswbr1q2Dj48PHnroIcP21NRUzJ8/H7m5uYbl22+/xd13322yx1OtX/3qVxg4cCBeeuklm8si0y233AIAFtv+NBfvyBCRm4sFoIXSJqZuo1YvAElwRO8lLy8vw7/6TU2/YsrixYshhDCMw9KtWze8+uqrWLBgAUaMGGH0iKm0tBQlJSVG7w8ODjaMGyKEQElJCYQQuHTpErKzs/Hyyy8jKCjIcHx7uHz5coNy+Pn52Tz+l0ajwR/+8Ac8//zzmDFjhqHHz7Vr1wzjmdQKCAjAo48+io0bN2Ly5Ml45ZVXkJiYiPLycrz55pvYunUrNm7ciDZt2gAA5s2bh08++QQPPPAAXnvtNcTHx+PcuXN4+eWX8cMPP5i801Vrz549qKqqanBXyprHHnsMy5Ytw5IlSzBu3DjDo53c3Fx8/fXX+OCDD9CzZ0+j94wbNw7Lli3Diy++aPKuFwDMnTsXo0ePxsKFC80+uqv9zOsLCwtrVuJq6bv20EMP4c4778TgwYMRHh6OoqIiLF68GLGxsQ3qaU/OkYYTEalqPZSkpa6kX9Y7RmBgoM0X9P379+PNN99EWlqaUffdGTNmYPDgwZg2bZpRe4+kpCREREQYLXXHUCkvL0dERAQ6deqEhIQEvPPOO5g8eTK++eYbu85dt3Tp0gblWLhwYaOOMXnyZFRVVeEf//iHYZ1Op8Ott95qtMyYMQMajQYffvghlixZgtdffx1xcXG4++678dNPP2Hfvn2GAQYBoFWrVtizZw9++9vfYsmSJYiOjkZKSgq8vLxw6NAh3HHHHWbL1KZNm0YnMQDQpUsXJCUl4eLFi0aPpVJTU3HLLbeYvLiPHj0a58+fx/bt280eNyUlBVFRURbvytR+5vWX8+fPN7oedVn6rmm1Wnz66ad44IEHEBsbi8mTJ6Nnz57YuXOn2aTMHjSisa2fXEx5eTmCgoKg1+s5KjCRC6qoqEBRURGioqIMdxiaLh9KmxjHjCNDRJZZ+n3bev3moyUi8iAxYAJD5F74aImIiIhcFhMZIiIicllMZIiIiMhlMZEhcgI6HbBjB5CfL7skxl56CRg2DLBjD90mx1S7X0JxMXDihPJfR6moAPR65b+OJCOurLqSc7PH75q9logkKisDxo8HMuuMoK/VAuvXA23byivXnj1AUhJQ96+DRgPs2wcMGeLomNVo316HsLAwqyPQNkV5uZJI1hcXB9Qbld9ubtwATp5UYtcKDAS6dwdU7KUqJa6supJrKC0txfnz5xEbG9tgjCVbr99MZIgkSkkBsrKAupMPe3kpF/SMDHnlatHCOKGopdEANTWOj/nf/xbj0qVLCAsLg5+fn9W5gRojL8/8tj597BbGSFERYGqg0zZtAJVGcZcWV1ZdybkJIXD16lWcP38ewcHBJsczYvdrIien0xnfialVXa2sz88HYiT0FH7pJdMJBaCsX7ECMDMJs2ox16wJx+TJaPZgXvXp9cClS+a35+YCQUF2DYmqKuDsWdPbLlxQtjdizkSnjiurruQ6goODER4e3qxjMJEhkqTQylyGBQVyEpnduy1v37nT/omM9ZgaLFoUgbCwMFRVVdkt7uTJQE6O+e3x8cCaNXYLBwD4/HNg5kzz2999V53HdzLiyqoruQZvb2+bp+ywhIkMkSQ9rMxlqMLEtTZJTAT27jW/ffhweTG9vLzs8oevVr9+wIcfmt8+cybQ7MGE6+nWDfjpJ/Pbo6LsH1NWXFl1Jc/CNjJEErGNjNyYsuLK+txlxHXW7zg5P1uv3+x+TSTR+vXKH/S6kpKU9TLt26dcyOuq7bXkTjFlxZX1ucuI66zfcXIfvCND5ATy85U2MdHRctrFmLNihdImZvhw+7eLcaaYsuLK+txlxHXW7zg5L3a//gUTGSIiItfDR0tERETk9pjIEBERkctiIkNEREQui4kMERERuSwmMkREROSymMgQERGRy3KaRGbFihXQaDSYO3euYV1FRQVmz56N0NBQ+Pv7Y+zYsTh37py8QhJBmexxxw5lXAxHmTcP6N8fWLDAcTEBOXX1JKmpwKRJQHq6+8fNzASWLQN27XJcTPIQwgl89dVXolu3bqJfv37iySefNKyfOXOmiIyMFLt37xZHjhwRd9xxhxg8eHCjjq3X6wUAodfr7Vxq8jSlpUJotUIoA9ori1YrRFmZejE3bzaOV7ts26ZeTCHk1NWTHDkihLe38fn19hbim2/cL25BgRChocYxQ0OFOHlSvZjkHmy9fksfEO/nn3/GbbfdhrfeegsvvvgiBgwYgJUrV0Kv16N9+/ZYt24dHnroIQDAf/7zH/Tq1QvZ2dm44447bDo+B8Qje5ExZ0z9ofPrUvOXy/lx1OXjA5iaxNvbG7h+3b3itmsHlJY2XB8aCly4oE5Mcg8uMyDe7NmzMXLkSCTVm4zj6NGjqKqqMlrfs2dPdOnSBdnZ2WaPV1lZifLycqOFqLl0OuXWeN0LO6C8zsxU59HLvHmWt6v1mElGXT1JaqrpZAJQ1qv1uEdG3MxM00kMoKznYyayB6mJzIYNG/D1119j+fLlDbaVlJTAx8cHwcHBRus7dOiAkpISs8dcvnw5goKCDEtkZKS9i00eqLDQ8vaCAvvH3LPH8na1LgIy6upJrE1GuXu3+8TNybG83cK/SYlsJi2ROX36NJ588kl88MEHaNWqld2Ou3jxYuj1esNy+vRpux2bPFePHpa3R0fbP+awYZa3JyfbPyYgp66eZOhQy9sTE90nbny85e0JCfaPSZ5HWhuZLVu2YPTo0fDy8jKsq66uhkajQYsWLZCZmYmkpCRcvHjR6K5M165dMXfuXMyzdt/9F2wjQ/bCNjJsI2MvbCPDNjJkndO3kUlMTMSxY8eQm5trWG6//XZMmDDB8P/e3t7YXed+54kTJ3Dq1CkkMI0nCdavVy7kdSUlKevVsm1b49bbi4y6epKvvlKSh7q8vZX17hb38GElaakrNFRZT2QP0nst1TV06FBDryUAmDVrFrZv34709HQEBgZizpw5AICDBw/afEzekSF7y89X2olERwMxMY6JuWCB0iYmORl49VXHxATk1NWTpKcrbVMSE4EpU9w77q5dSpuYhAT1HouSe7H1+u3UiUxFRQXmz5+P9evXo7KyElqtFm+99RbCw8NtPiYTGSIiItfjkomMGpjIEBERuR6nbyNDRERE1FxMZIiIiMhlMZEhIiIil8VEhoiIiFwWExkiIiJyWUxkiIiIyGW1lF0AV6XTKZPrecJAYbLqmpmpTDrnyAG0ZMSUGdcZyfq+edJvmsidMJFppLIyYPx45cJTS6tVhm5v21ZeudQgq66Fhcpkc3XnZ6kd0jwqyn1iyozrjGR93zzpN03kjjggXiN50mR6suoqY5I5WRPbcUK9m2R93zzpN03kSjggngp0OuVfbXX/4AHK68xMZV4adyGrrpmZpi/sgLJ+1y73iCkzrjOS9X3zpN80kbtiItMIhYWWtxcUOKYcjiCrrjk5lrdnZ7tHTJlxnZGs75sn/aaJ3BUTmUbo0cPy9uhox5TDEWTVNT7e8vaEBPeIKTOuM5L1ffOk3zSRu2Ii0wixsUojQC8v4/VeXsp6d+rpIKuuWq3SPsSU0FB1evTIiCkzrjOS9X3zpN80kbtiItNI69crjQDrSkpS1rsbWXU9fLjhBb62J487xZQZ1xnJ+r550m+ayB2x11IT5ecrz889YcwJWXXdtUtpJ+LIsVVkxJQZ1xnJ+r550m+ayBXYev1mIkNEREROh92viYiIyO0xkSEiIiKXxSkKiMgszj9ERM6Od2SIqIGyMmXo/rg44L77lG7KKSnAxYuyS0ZEZIyJDBE1MH68Mv9QXVlZwLhxcspDRGQOExkiMsL5h4jIlTCRIat0OmDHDsdfwF56CRg2DFixwrliqlEua8ecNw/o3x9YsMB+Mc2RPf9QaiowaRKQnq5uHGeIK+M7Dsj7TROpQrg5vV4vAAi9Xi+7KC6ntFQIrVYI4Oai1QpRVqZu3N27hdBojONqNELs3y83phrlsnbMzZuNt9Uu27Y1q7oWnThhOmbtotOpE/fIESG8vY1jeXsL8c036sSTGVfGd1wIeb9poqaw9frNAfHIrJQUpV1E3UcMXl7K8O0ZGerFbdFC+RNbn0YD1NTIi6lGuawdU6Mx/141f7ky4vr4AFVVDdd7ewPXr6sTU1ZcGd9xQN5vmqgpOCAeNYusdhIvvWT+QimEOrfgbYmpRrmsHfPOOy2/X63HTC+9ZHm7Gp9BaqrpZAJQ1qv1uEdGXBnfcYBtn8h9MZEhk2S1k9i92/L2nTvlxFSjXNaOmZtrefuuXY2PaQsZn8G+fZa3WyuTK8WVcX4B+W2fiNTCRIZM6tHD8vboaHXiJiZa3j58uJyYapTL2jEHDLC8Xa3JJWV8BkOHWt5urUyuFFfG+QXk/aaJ1MY2MmQW28h4bhsZGZ8B28iwjQxRXWwjQ822fr3yB66upCRlvZr27Wt4AddorD8GUDumGuWydsxt20y/z9x6e5HxGXz1lZI81OXtraxXk4y4Ms4vIO83TaQm3pEhq/Lzlefnjp5vZ8UKpb3A8OHAokXOE1ONclk75oIFSpuY5GTg1VftE9Me5VJDerrSjiQxEZgyxTExZcWVcX4Beb9posaw9fotddLIVatWYdWqVfjxxx8BAL1798bSpUsxYsQIAMDQoUOxf/9+o/fMmDEDb7/9tqOL6tFiYuT8sVu0yLF/3G2N2b490KkTEB7uuLj1e5o4ioy4//2vspSUODbulCmOTZwAOd9xQM5vWtYEpJz41P1JvSPz6aefwsvLCzExMRBCYM2aNXjllVfwzTffoHfv3hg6dChiY2OxbNkyw3v8/PwadWeFd2TIXo4eBRISjNtT1D6CsNYwt6m2bAFGj264fts2YORIdWICwJ49yiOHun8dah99DBniPjFJfWVlytxdmZk312m1yuOstm3dLy7Zj63Xb6d7tBQSEoJXXnkF06ZNw9ChQzFgwACsXLmyycdjIkP2IqNRqCc19pXVAJbUJauBMRs2uz6Xa+xbXV2NDRs24MqVK0hISDCs/+CDD9CuXTv06dMHixcvxtWrVy0ep7KyEuXl5UYLUXPJGDht3jzL29UcEM8ZByUk1yNrED4O/udZpCcyx44dg7+/P3x9fTFz5kxs3rwZt9xyCwBg/Pjx+Ne//oW9e/di8eLFeP/99zFx4kSLx1u+fDmCgoIMS2RkpCOqQW5OxsBpe/ZY3u5OA+LJGiSO1CVrED4O/udZpCcycXFxyM3NRU5ODmbNmoXJkyfj+PHjAIDp06dDq9Wib9++mDBhAtauXYvNmzej0MK3dPHixdDr9Ybl9OnTjqoKuTEZA6cNG2Z5uzsNiCdrkDhSl6xB+Dj4n2dxujYySUlJ6NGjB955550G265cuQJ/f39kZGRAq9XadDy2kSF7YRsZtpGhxmMbGWoql2sjU6umpgaVlZUmt+X+MvlMRESEA0tEpJAxcJonDYgna5A4UpesQfg4+J8HERItWrRI7N+/XxQVFYnvvvtOLFq0SGg0GrFz505RUFAgli1bJo4cOSKKiorEJ598Irp37y6GDBnSqBh6vV4AEHq9XqVaONZ77wkxcaIQaWnuHVOWEyeE2L5dCJ3O/D5TpgjRrZsQU6c6Lu7UqfaPaYv584Xo10/5rzvHFEKIyZMdf45lxBRCiLlzHX+OX3pJiHvvFWL5csfFlBmXms/W67fURGbq1Kmia9euwsfHR7Rv314kJiaKnTt3CiGEOHXqlBgyZIgICQkRvr6+Ijo6Wjz99NONTkjcJZE5ckQIb28hlJvvyuLtLcQ337hXTFlKS4XQao3rqtUKUVZ2cx81zoe1uLaUSw0y4sqq69q1xjFrlw0b3CumEEJs3mw67rZt6sWU9XfEk/5+uStbr99O10bG3tyljYyM9hmyJvGTwZbn6WqcD2txPal9gay6ymiHJKvtk4y4njQZKNmXy7aRoYZkjGEiI6Ystow5ocb5sBZ3507PGYND1rgf1qYkmDbNPWICcsYlkvV3xJP+fhETGZcgYwwTGTFlsWXMCTXOh7W4hw5Z3u5OY3DIGvej3lRuDVgby8dVYtpyXDXGJZL1d8ST/n4RExmXIGMMExkxZbFlzAk1zoe1uHfcYXm7O43BIWvcj3vusbzd2lg+rhLTluOqMS6RrL8jnvT3i5xwHBl7YxsZ14opC9vINK5c7hITYBsZteOyjQw1FdvIuBkZY5jIiCmLLWNOqHE+rMX1pDE4ZNV1w4bGrXfVmICccYlk/R3xpL9fno53ZFxMerryfDcx0XqjQVeOKUt+vtIeIzoaiIkxvU9yMnD4MBAfrzREtYedO5U2MQkJpm/xP/aY8tx/2DClIaOjjBkDfPklcPfdwEcfOSamGufXFtHRQFEREBXluLl4evdWGjrHxgLff++YmABw223A8eNK/KNHHRNT1t8RT/r75W5svn6r3hFcMncZR4bkU2MMDmtjp8gY90MIOeOcvPWW6ZjvvadeTCHk1FXW5yorLlFTcByZX7jbHRmSR432BdbahXhSWwrWVd2YMuMSNQXbyBDZkRpjcFgbO8XabXA1xv0A5IxzMnq05e0PPWT/mICcusoYz0VmXCK1MZEhsoEaY3BYGzvF2ngjaoz7YUtcNcY5OXjQ8vYDB+wfE5BTVxnjuciMS6Q2JjJENlBjDA5rY6dYG29EjXE/bImrxjgngwdb3n733faPCcipq4zxXGTGJVJbs9rIXL9+HUVFRejRowdatmxpz3LZDdvIkL2wjYy6cVlXdWPKjEvUFKq2kbl69SqmTZsGPz8/9O7dG6dOnQIAzJkzBytWrGhaiYmcnBpjcFgbO0XGuB+AnHFO3nuvcevtRUZdZX2usuISqaopXaL+8Ic/iIEDB4oDBw6INm3aiMLCQiGEEFu2bBEDBgxoyiFVw+7XzXfihBDbtwuh03lGXGvmzxeiXz/lv/ai01muqxoxbTF1qhDduin/dZSxY4UIC1P+60gy6iojphDyvk9EjaFq9+uuXbvi3//+N+644w4EBATg22+/Rffu3VFQUIDbbrsN5eXl9s+4moiPlpqurAwYP954UDKtVrlb0Lat+8UlchR+x4msU/XR0v/+9z+EhYU1WH/lyhVoLD2EJZcyfrzSfqOurCxg3Dj3jEvkKPyOE9lPkxKZ22+/HZ999pnhdW3y8t577yEhIcE+JSOprI1xkp/vXnGJHIXfcSL7alJXo5dffhkjRozA8ePHcePGDfztb3/D8ePHcfDgQey3NjADuQRrY5wUFJifi8gV49qTTqfUw9J8TeS53OE7TuRMmnRH5q677kJubi5u3LiBvn37YufOnQgLC0N2djYGDhxo7zKSBNbGOImOdq+49lBWpnSnjosD7rtPmQgwJQW4eFF2yciZuPJ3nMgZca4lMsvaGCfuFre5XLXc5Hj8rhBZp2pj3+3btyOzbnP7X2RmZmLHjh1NOSQ5IWtjnLhb3OZguwdqDFf8jhM5qyYlMosWLUJ1/b/YAIQQWLRoUbMLRc6hbVvlX4c6HbB9u/LfjAz1u4fKitsctrR7IKrlit9xImfVpMa++fn5uOWWWxqs79mzJwr4F9vtxMTIaXwoI64tDXV1pToUlhUiOiQaMaHKTmq3ezAV0xFkxPWkup7U63D4UiFalkcjBo6ra+YRHXLyC5EQG43kgWxZTK6tSYlMUFAQTp48iW7duhmtLygoQJs2bexRLiKHsmWAsrJrZRj/8XhkFt7cSdtDi/Vj1yM2ti20WvPtHpqakFmK2ba1ev98lxHXk+paeLYM8X8Zj9K2v8TUAaHva3H4mfWIilCvrrLiEqmpSY+WHnzwQcydOxeFde6nFxQUYP78+fj1r39tt8IROYotA5SN/3g8sk4a75R1MgvjPlZ2stTuQacDduy42Vam/muz5bISUy0y4npSXeP/Mh6lQcYxS4OyMOjP6tZVVlwiNTWp15Jer0dKSgqOHDmCzp07AwDOnDmDu+++G5s2bUJwcLC9y9lk7LVE1uh0SpdpS9tFiA5x/zC/k+4JneFxRH6+0iYmOhoIDW14pyc0FCgtvfna3ND0ulLbY9qTjLieVNfMIzqkfGY+5s77dao87pEVl6ipVO21FBQUhIMHD+Kzzz7D448/jvnz52P37t3Ys2ePUyUxRLawpaFuYZnlnQrKbrYNi4kBRoxQ/mvqTk/dJAYwPzR9Y2Lak4y4nlTXnHzLMbN16tRVVlwitTWpjQygTEswfPhwDB8+3J7lIXI4WxrqihDLO0WHNGzNW9sl25q6XbTrtqXp0YSY9iAjrifVNT6mB6Azvz0hVp26yopLpDabE5k33ngD06dPR6tWrfDGG29Y3PcPf/hDswtG5CixsbChoW4stD20yDqZhWpxcycvjReSuieZfPxg7U5PffWHpo8NbXxMe5AR15Pqqr09FqH/0iptVVrU+cLVeCFUn6Ta4x1ZcYnUZnMbmaioKBw5cgShoaGIiooyf0CNBidPnrRbAZuLbWTIFhcvKo93LPVaunjtIsZ9PM7m3i3W2t6Y2r9+76bGxrQXGXE9qa5FxRcx6M/jbvYeAhB6Uf3eQ7LiEjWFrddvqVMUrFq1CqtWrcKPP/4IAOjduzeWLl2KESNGAAAqKiowf/58bNiwAZWVldBqtXjrrbfQoUMHm2MwkXFdqanAvn1AYiIwZYpjYr78snJnZvhwwNzYjqu35GPvtwVIvDUaU37d8F+xmZlATg6QkAC89lrDOz31eXkBnToBwcFAcjLw6qvGx0hOBl5+Ox9Z3xRg+O3RWPR7x/3Led6L+diTW4Dk26Lx6hLHxJVVVxlxl7+Tj11fO76uu47mI1tXwHFkyKnZfP0WjXT9+nXRvXt3cfz48ca+tYGtW7eKzz77TOh0OnHixAmxZMkS4e3tLfLy8oQQQsycOVNERkaK3bt3iyNHjog77rhDDB48uFEx9Hq9ACD0en2zy0uOceSIEN7eQgA3F29vIb75Rm7MggIhQkON9wkNFeLkSfPbAwOFaNnSeF1zF7XPhRBC7N4thEZjHFejEWL/fvViyvjcZcW19l0iItuv3026I9OpUydkZWWhV69eTU+1zAgJCcErr7yChx56CO3bt8e6devw0EMPAQD+85//oFevXsjOzsYdd9xh0/F4R8b1+PgAVVUN13t7A9evy4vZrl3DHkeA0p36wgXz29Wg5rkAgBYtlMtrfRoNUFOjTkwZn7usuNa+S0Skcvfr2bNn489//jNu3LjR5ALWV11djQ0bNuDKlStISEjA0aNHUVVVhaQ6I4z17NkTXbp0QXZ2ttnjVFZWory83Ggh15GaavqiAijr09PlxMzMNJ+klJYCy5c7LompWy41vPSS6SQGUNavWGH/mDI+d1lxrX2Xdu2yf0wid9akRObw4cPYtGkTunTpAq1WizFjxhgtjXHs2DH4+/vD19cXM2fOxObNm3HLLbegpKQEPj4+Dcal6dChA0pKSsweb/ny5QgKCjIskZGRTakiSbJvn+Xtu3fLiZmTY3kfGRcfNc6FLcfdudP+MWV87rLiWvsuWfh3GhGZ0KREJjg4GGPHjoVWq0XHjh2NEoegoKBGHSsuLg65ubnIycnBrFmzMHnyZBw/frwpxQIALF68GHq93rCcPn26yccixxs61PL2xEQ5MePjLe+TnGy34thMjXNhy3HVGDpKxucuK66171JCgv1jErmzRrWRqampwSuvvIKtW7fi+vXrGDZsGJ5//nm0bt3abgVKSkpCjx498MgjjyAxMREXL140uivTtWtXzJ07F/PmzbPpeGwj43rYRsY6tpFx7bhsI0NknSptZF566SUsWbIE/v7+6NSpE9544w3Mnj272YWtq6amBpWVlRg4cCC8vb2xu8693RMnTuDUqVNI4D9Z3NpXXykXkbq8vZX1MmMePqxcaOoKDVXWm9seFNRwDiWNpnllVftcAMojl/rl1GisP4ppDhmfu6y41r5LRGS7Rt2RiYmJwYIFCzBjxgwAQFZWFkaOHIlr166hRYvGP6VavHgxRowYgS5duuDy5ctYt24d/vznPyMzMxPJycmYNWsWtm/fjvT0dAQGBmLOnDkAgIMHD9ocg3dkXFd6utJGwZHjyNgSc9cupR1D7Rgvtmyvv67+6wULlHW148jU3y7jXABKw96dOy2Pq2NvsuoqI6617xKRJ1NlQDxfX18UFBQYNaBt1aoVCgoKDLNgN8a0adOwe/duFBcXIygoCP369cMzzzyD5F9+0bUD4q1fv95oQLzw8HCbYzCRISIicj2qJDJeXl4oKSlB+/btDesCAgLw3XffWZy2QCYmMuQKdDplbqboaGWagvqviYg8ja3X70bNfi2EwJQpU+Dr62tYV1FRgZkzZ6JNmzaGdZs2bWpCkYk8T1kZMH688RxPoaHGDUHrz/lEREQ3NSqRmTx5coN1EydOtFthiDzN+PHKXEx11e/NkpWlTGiZkeG4chERuQqpk0Y6Ah8tkbOyx+zYRETuStUpCoio+QoLG7d/QYE65SAicmVMZIgk6dGjcftHR6tTDiIiV8ZEhqiRMjOBZcuaP79SbKzSkNfLy/J+Xl7AwIFKg19Hz+lkr7o6e0xZcWXVlcidNKqxL5EnKyxU5smp2xi3djTWpo4+sH690pDXUq+lFi2Ao0eVxR4xbaFGXZ0xpqy4supK5I7Y2JfIRmrOj5Ofr7SBqR03pvb1+PHApUvqxLRExlxAsuYf8qS6ErkSNvYlsqPMTPMTQpaWNv/RQEwMMGLEzV5JMTHKnRhTSYy9Ypqjdl2dJaasuLLqSuSumMgQ2SAnx/L27Gz3iCkrLuuqbkwid8Y2MkQ2iI+3vN3ahOxNmYKguTGbSkZc1lXdmETujG1kiGzUlHYNzZ2CgO1GWFciT8U2MkR2dviwcqGpq7aniTmNmYLAXjHtQUZc1lX9uhK5I96RIWqkXbuUdgwJCUBysvn97DkFga0x7U1GXNaViADbr99MZIhUsmMHcN99tu+/fbvSc4mIiPhoiUg6TkFARKQ+JjJEKmnMFARaLWe2JiJqCiYyRCpavx5ISjJeV7+RZ1KSsh8RETUex5EhUlHbtkBGhvkpCCyNI0NERNYxkSFygJgY44Sl/msiImoaPloiIiIil8VEhoiIiFwWExkiIiJyWUxkiIiIyGWxsS+RE2jK7NhERMREhkiq5s6OTUTk6fhoiUii5s6OTUTk6ZjIEEmi0yl3YqqrLe9XXa3sl5/vmHIREbkSJjJEkhQWNm7/ggJ1ykFE5MqYyBBJwtmxiYiaj4kMkZ3pdMCOHdYfBXF2bCKi5pOayCxfvhyDBg1CQEAAwsLCMGrUKJw4ccJon6FDh0Kj0RgtM2fOlFRiIvPKyoCUFCAuDrjvPiVRSUkBLl40/x7Ojk1E1DwaIYSQFTwlJQWPPvooBg0ahBs3bmDJkiXIy8vD8ePH0aZNGwBKIhMbG4tly5YZ3ufn54fAwECbYpSXlyMoKAh6vd7m9xA1RUqK0sOobuNdLy8lEcnIsPxezo5NRGTM1uu31HFkMur9dU9PT0dYWBiOHj2KIUOGGNb7+fkhPDzcpmNWVlaisrLS8Lq8vNw+hSWyoLYHUn11exxZSkg4OzYRUdM4VRsZvV4PAAgJCTFa/8EHH6Bdu3bo06cPFi9ejKtXr5o9xvLlyxEUFGRYIiMjVS0zEWC9BxJ7HBERqUPqo6W6ampq8Otf/xqXLl3CF198YVj/7rvvomvXrujYsSO+++47PPPMM/jVr36FTZs2mTyOqTsykZGRfLREqtLplLYxlrbzDgsRke1c4tFSXbNnz0ZeXp5REgMA06dPN/x/3759ERERgcTERBQWFqKHif6rvr6+8PX1Vb28RHXV9kAy10aGSQwRkTqc4tHSE088gW3btmHv3r3o3LmzxX3j4+MBAAW8V09OxlQPJPY4IiJSl9Q7MkIIzJkzB5s3b8a+ffsQFRVl9T25ubkAgIiICJVLR9Q4bdsqvZPY44iIyHGkJjKzZ8/GunXr8MknnyAgIAAlJSUAgKCgILRu3RqFhYVYt24d7rvvPoSGhuK7777DvHnzMGTIEPTr109m0YnMakqPo8xMICcHSEgAkpMbviYiItOkNvbVaDQm16elpWHKlCk4ffo0Jk6ciLy8PFy5cgWRkZEYPXo0nn32WY4jQ26hsBCIjzee8VqjAer+KkNDgcOHARtuWBIRuQ1br99O02tJLUxkyJm1a2ecxJgTGgpcuKB+eYiInIWt12+naOxL5IkyM21LYgBlv1271C0PEZErYiJDJElOTuP2z85WpxxERK6MiQyRJL+MJGCzhAR1ykFE5MqYyBBJotU2nOnanNBQ9l4iIjKFiQyRRIcPN0xm6nfmq+21REREDTnNFAVEnigqSumNtGuX0gamdtyY+q+JiMg0dr8mIiIip8Pu10REROT2mMgQERGRy2IiQ0RERC6LiQwRERG5LCYyRERE5LKYyBAREZHL4jgyRHXpdEBhIRAdDcTEuH9cIiIXxzsyRABQVgakpABxccB99wGxscrrixfdMy4RkZtgIkMEAOPHA1lZxuuysoBx49wzLhGRm2AiQ6TTAZmZQHW18frqamV9fr57xSUiciNMZIgKCy1vLyhwr7hERG6EiQxRjx6Wt0dHu1dcIiI3wkSGKDYW0GoBLy/j9V5eynq1ehHJiktE5EaYyBABwPr1QFKS8bqkJGW9O8YlInITGiGEkF0INdk6DTgRAKWBbUGB48dzkRWXiMhJ2Xr95oB4RHXFxMhJJGTFJSJycXy0RERERC6LiQwRERG5LCYyRERE5LLYRoaIzJMxmaUnTdzJurpnXE+qqzMQbk6v1wsAQq/Xyy4KkesoLRVCqxUCuLlotUKUlblXTFlxWVfW1R3iqszW6zcTGSJqSKsVwsvL+A+jl5ey3p1iyorLurKu7hBXZbZevzmODBEZ0+mAuDjL2+1921pGTFlxWVd1Y8qK60l1dRBbr99SG/suX74cgwYNQkBAAMLCwjBq1CicOHHCaJ+KigrMnj0boaGh8Pf3x9ixY3Hu3DlJJfZQOh2wY4fjZ2OWEVdWXZ2JjMksPWniTtZV3Ziy4npSXZ2M1ERm//79mD17Ng4dOoRdu3ahqqoKw4cPx5UrVwz7zJs3D59++ik2btyI/fv34+zZsxgzZozEUnuQsjIgJUXJ9u+7T5kbKCUFuHjR/eLKqqszkjGZpSdN3Mm6qhtTVlxPqquzcciDLhudP39eABD79+8XQghx6dIl4e3tLTZu3GjY54cffhAARHZ2tk3HZBuZZvCk571u+oy5yTzpM2BdWVdXjikzrspcsrFvfn6+ACCOHTsmhBBi9+7dAoC4ePGi0X5dunQRf/3rX00eo6KiQuj1esNy+vRpJjJNceKE8Y+i/qLTuU9cWXV1ZmVlju8FISOmrLisK+vqDnFVZmsi4zTjyNTU1GDu3Lm488470adPHwBASUkJfHx8EBwcbLRvhw4dUFJSYvI4y5cvxwsvvKB2cd2fLc9d1WhAJiOurLo6s7ZtgYwMx05mKSOmrLisq7oxZcX1pLo6EadJZGbPno28vDx88cUXzTrO4sWL8dRTTxlel5eXIzIysrnF8zye9LyXz5jNkzGZpSdN3Mm6umdcT6qrE3CKKQqeeOIJbNu2DXv37kXnzp0N68PDw3H9+nVcunTJaP9z584hPDzc5LF8fX0RGBhotFATxMYCWi3g5WW83stLWa/Wj0VGXFl1JSKiZpOayAgh8MQTT2Dz5s3Ys2cPoqKijLYPHDgQ3t7e2L17t2HdiRMncOrUKSQkJDi6uJ5n/XogKcl4XVKSst7d4sqqKxERNYvUAfEef/xxrFu3Dp988gni6gzoExQUhNatWwMAZs2ahe3btyM9PR2BgYGYM2cOAODgwYM2xeCAeHawcydw6BCQkAAkJ7t3XFtiqjGfibVjetLcLayr+8YlagSbr9+OaHlsDgCTS1pammGfa9euiccff1y0bdtW+Pn5idGjR4vi4mKbY7D7dTN40rwhtsRUo1zWjsnPgHV1h7hETeCS3a/VwESmGTxpTARbYqpRLmvH5GfAurpDXKIm4FxLv+CjpSbypHlDbIkphP3LZS1uZqbS2NieMe1RLs5T43oxZcYlaiKXmGuJnJgnzRtiS0w1ymXtmIcO2T+mLZz1M1AD66p+XCKVOc04MuRkOI6McUxrNy6bUi5rce+4w/4xbeGsn4EaWFf14xKpjHdkyDSOI2McU41yWTvm8OH8DFhX149LpDaHtNiRSLXGvidOCLF9u+Pn4XFkXNnzhsRAiBQIEe3AOVIsxVTjfFg7puzPgPPUuE9MmXGJmsDl5lpyGWVlwPjxSkPMWlqtMnBa27buFVdaO/Ay4MUjwO11Vh05AmguAVDrHNsQU435TKydY0+au4V1VTemzLhEKmKvpcZKSQGysoDq6pvrvLyUUWAzMpp/fGeKK6uuR9oBA0qNW3DdAJAbCtx+wX1iAvLOMRGRk7P1+s1EpjE8qdukrLoWZQJRKea3/7gT6GbnUX5lxATYHZaIyAJ2v1aDJ3WblFXXCzmWt/8v2z1iAuwOS0RkB0xkGsOTuk3Kqmu7eMvb26swWaiMmAC7wxIR2QETmcbwpG6TsuoapQWOhCrtU+q6AWW9Go94ZMQE2B2WiMgOmMg01vr1SkPMupKSlPXuFldWXaMPK41s68oNVda7U0xA3jkmInIT7H7dWLLaRntSF9GaIODZ24HCTCAaQAGAHrcD64PdKyYgsYs7EZF7YK+lxmJ3WfV5Uldzfp+IiExi9+tfuEX3a0/iSV3N+X0yTadTenQ5erA2GXFZVyKz2P1aDewuqz5P6mrO75OxsjLlDlVcHHDffUpj6JQU4OJF94vLuqpfV/IYTGQag91l1edJXc35fTI2frzymK2urCxg3Dj3i8u6ql9X8hhMZBqD3WXV50ldzfl9ukmnU+YRq9tWCFBeZ2YqDc7dJS7rqn5dyaMwkWksdpdVnyd1Nef3SeFJj/dYV3Vjksdh9+vGkj17rCc00qs9xy+/rNyCHj4cWLTIMTFXrwb27gUSE4EpU0zva8/zURt3507g0CEgIQFINjEAn6SGki/NO4/de1pgeLLAolfbqxeozmO2TCQjB/FIQDaSsVtZ6U6P9zzpUSYfn5IjCDen1+sFAKHX62UXpXlKS4XQaoVQRh5RFq1WiLIy94ophBBHjgjh7W0c19tbiG++US9mQYEQoaHGMUNDhTh58uY+apwPa8eU9Bns3qwXGlQbhdWgWuzfdkm1mAV3TRahOG/8EeC8OHn3b1WLKYRQzqeXl/E59vJS1rtTTFlxZdWVXJ6t128mMq7Ck/4A1U9i6iYzaqmfxNRNZmqpcT6sHVPSZ6AkMTX1TkeN0KBatZihIaZjhoaoF1MIoSSFjk4WZcSUFVdWXcnlMZH5hVskMidOmL7I1i46nXvEFEKI996zHDctzf4xMzIsx9y5U53zYe2YmZlSPoMX556zGHb5/PN2j2nLR6AaWXcehVA+w+3b1fs9OVNcWXUll2Xr9ZuNfV2BJzXS27fP8vbdu+0fMyfH8vbsbHXOh7VjHjpk/5g22L3H8p+Fnbs0do9py0egGpndg2NigBEjHN9DTUZcWXUlt8dExhV4UiO9oUMtb09MtH/M+HjL2xMS1DkfdY6pQwx2IAX5qHOcO+6wf0wbJN5aanH78NvL7B4zvtNpi9sTupyxe0wA7B5M5AaYyLgCTxpbZdo0wNvb9DZvb/M9iZpDq7UcMzlZOR+hoab3CQ1t2vmIjUXZvWORggzEQYf7sAOxyEcKMnBx2Filt5bGzN0PjUa1z+D/HjkJDaoBiHpbBDSoxqKHrNxJagJtxzyE4n8mY4bif0jucMzuMQGwezCRG2Ai4yo8aWyVr75qmFh4eyvr1aDTAVVVprdVVSn/KtfpgFIzdypKS5v8L/fxmvXI0hif4yxNEsZhPZCaqrTYMEUIID29STGt+vpr7MMQaFBjtFqDGuzDEODbb1WJeRiDEIoLRqtDcQGHMUidmID87sHz5gH9+wMLFqgbp76XXgKGDQNWrHBczNRUYNIk9b63zhQ3MxNYtgzYtctxMWXFlVXXuhzUZkcat2jsW5cnNdJLSxNi4kR1GvjWtX275Zam27fbtk8jWW0//Ov5lneYOFGFkyGU4/4SYzmeFvdil1iOp9WNWyfmTiSKF/Cs2IlE9esqhBADBpg+vwMGqBdz82bTMbdtUy+mEELs3i2ERmMcU6MRYv9+9WLKGE5BVlxbhnFwl7gOiGnr9ZuzXxPZMgu1EHafqXrHDmUOPXO2P5mJEX9LMb9DWpo6j9pSU4Hf/c6xcWXErGXu8R1g/o6YK8YEgBYtTB9fowFqahqutwcfH9N3PL29gevX1YkpK267dqbv3IaGAhcuNFzvynEdEJOzXxPZypb2QAcOWD7Gl182OqzVpxr9/Czv0KlTo2PapHNnx8eVERNQHu1YosYjHxkxAeVxkqVHlWo8ZkpNtfzYVq3HPTLiZmZafvys1qMXGXFl1dUMqYnM559/jgceeAAdO3aERqPBli1bjLZPmTIFGo3GaElJsfAvVKKmstYeSIVu4VbzpzN7LR9ArT7JMvpCy+p/vWeP5e1q/EGWEROw/h3dudP+MWUMpyArrqzvsCf9Xs2QmshcuXIF/fv3x5tvvml2n5SUFBQXFxuW9Z42mR45Ru2cRzodsH278t+MDGU9oFq3cIv5ky3dwtUgI66sug4bZnm7qXmvXDEmYP07Ony4/WPKGE5BVlz+XtWNaYHTtJHRaDTYvHkzRo0aZVg3ZcoUXLp0qcGdmsZgGxmyGxWfuZudg5TP3NWvK9vIsI2MvfD3yjYypuzbtw9hYWGIi4vDrFmzUGruudwvKisrUV5ebrQQ2YWK3cLNDnp6+HDD8WtCQ5X1apIRV1Zdt21r3Prm0uksb1dzEL59+xomURqN9UcxzeHo4RRkxuXvVf26muDUd2Q2bNgAPz8/REVFobCwEEuWLIG/vz+ys7PhVb9hwS+ef/55vPDCCw3W844M2SQzU3n+m5Bg/hZ/erryjD0x0X49aazF3bVLee5sqVxqkBFXVl0XLFBiJycDr76qXhyr3dW2K1mtmlasUNrEDB8OLFqkbqxaavxunDUuf692YesdGadOZOo7efIkevTogaysLCSaecZZWVmJyspKw+vy8nJERkYykSHLCguV57517/jV/usiKsr94pI8tnT353xERO7zaKmu7t27o127diiwMGy4r68vAgMDjRYiq+onE4DyetAg94xL8sia/oPITblUInPmzBmUlpYiIiJCdlHInXjS+A/kHGRN/0HkhlrKDP7zzz8b3V0pKipCbm4uQkJCEBISghdeeAFjx45FeHg4CgsLsXDhQkRHR0Or1UosNbkdW8ZEqPvs15Z2NPaOa6+YjSUjrifUtba7/+rVwN69jm83ws/V/WLKiqvTKY/IG3S5dCC7TYrQBHv37hVQprs1WiZPniyuXr0qhg8fLtq3by+8vb1F165dxe9//3tRUlLSqBhuN9cS2V9GhuU5jXbuVPaz99witsTl3C2sq6vHZV3ds66lpUJotcYxtVohysrsFoJzLf2C48iQTWwZE0GNcROsHZPjUrCurh6XdXXPuqakAFlZQHX1zXVeXsoj0owMu4Rwy8a+RKqxNiaCWu1ZLMX1pLY7rKu6MWXFZV3VjSkrrk6nxK2bxADK68xMdcdCMoGJDBGgdHW+cEEZW+OFF5T/Xrhwswu0WnOLBAUBt99uvO7224HgYM7donZMWXFZV3VjyorrSXUtLLS83ULPYjVIbexL5HSSk003klNrbpHx45Xbs3VlZQHjxlmfJZlzt7hmXNZV3Ziy4npSXXv0sLw9Otr+MS3gHRkiW1gbnK5bt8Yf09rt2e7dGz52qhUaql6vBDXq6owxZcWVVVetFggJMb0tJESd75OMmLLiyqyro/9OONlYSExkiGyhxq1UW44pYz4TGbeNZd2q9qS6AkDv3o1b76oxZcWVVVcZfyecaCwkPloisoUat1JtOWZt2x1HzqEi47axrFvVnlRXnQ44cMD0tgMHlAaa9v6XtIyYsuLKqisg5+9E7VhI+flK8i1xHBnekSGyhRq3UhtzzORkYOlSxwxyJeO2saxb1Z5UV0+6++RJda3LkX8nasXEKJOcSpxag4kMka3UuJXqRLdnjcgol6xz4Sl19aS7T55UV3Ke2a/VwgHxyO7UuJXqBLdnTZJRLlnnwhPq6oBBzJwipqy4surqpmy9fjORISLyFBcvKl37MzNvrtNqlTtBbdu6T0xZcWXV1U0xkfkFExkiono84e6TzLjOeofVxTCR+QUTGSIiItfDuZaIiIjI7TGRISIiIpfFRIaIiIhcFkf2JSLnotMpg4uxoSQR2YB3ZIjIOZSVKeNwxMUB992njICbkqJ0aSUiMoOJDBE5h/HjlcHE6srKUsblICIyg4kMEcmn0ymDiNUdERVQXmdmKuNyEBGZwESGiORzhgn3iMglMZEhIvk44R4RNRETGSKSLzZWmZPGy8t4vZeXsp69l4jIDCYyROQc1q9XZgmuKylJWU9EZAbHkSEi59C2LZCRIW/CvcxMICcHSEgAkpPdNybAsXrIrTCRISLnEhPj2ItrYSEQHw+Ult5cFxoKHD4MREW5T0xAGatn/Hglgaql1Sp3vdq2VS8ukYr4aImIPFv9hAJQXg8a5F4xAY7VQ26JiQwRea7MzIYJRa3SUmDXLveICXCsHnJbTGSIyHPl5Fjenp3tHjEBjtVDbouJDBF5rvh4y9sTEtwjJsCxeshtMZEhIs+l1SqNbE0JDVWnJ5GMmADH6iG3xUSGiDzb4cMNE4vaHkTuFBPgWD3klqQmMp9//jkeeOABdOzYERqNBlu2bDHaLoTA0qVLERERgdatWyMpKQn5bJBGRPYUFQVcuADs3Am88ILy3wsX1O0GLSMmcHOsHp0O2L5d+W9GBrtek0uTOo7MlStX0L9/f0ydOhVjxoxpsP0vf/kL3njjDaxZswZRUVH44x//CK1Wi+PHj6NVq1YSSkxEbis52bGD0smKCTh+rB4iFUlNZEaMGIERI0aY3CaEwMqVK/Hss8/iwQcfBACsXbsWHTp0wJYtW/Doo4+afF9lZSUqKysNr8vLy+1fcCIiInIKTttGpqioCCUlJUiq8zw3KCgI8fHxyLbQPXH58uUICgoyLJGRkY4oLhEREUngtIlMSUkJAKBDhw5G6zt06GDYZsrixYuh1+sNy+nTp1UtJxEREcnjdnMt+fr6wtfXV3YxiIiIyAGc9o5MeHg4AODcuXNG68+dO2fYRkRERJ7NaROZqKgohIeHY/fu3YZ15eXlyMnJQYJaI18SERGRS5H6aOnnn39GQZ35PYqKipCbm4uQkBB06dIFc+fOxYsvvoiYmBhD9+uOHTti1KhR8gpNRERETkNqInPkyBHce++9htdPPfUUAGDy5MlIT0/HwoULceXKFUyfPh2XLl3CXXfdhYyMDI4hQ0RERAAAjRBCyC6EmsrLyxEUFAS9Xo/AwEDZxSEiIiIb2Hr9dto2MkRERETWuF336/pqbzhxhF8iIiLXUXvdtvbgyO0TmcuXLwMAR/glIiJyQZcvX0ZQUJDZ7W7fRqampgZnz55FQEAANBqN1LKUl5cjMjISp0+fZnsdO+D5tD+eU/vi+bQvnk/7c+ZzKoTA5cuX0bFjR7RoYb4ljNvfkWnRogU6d+4suxhGAgMDne4L48p4Pu2P59S+eD7ti+fT/pz1nFq6E1OLjX2JiIjIZTGRISIiIpfFRMaBfH198dxzz3FSSzvh+bQ/nlP74vm0L55P+3OHc+r2jX2JiIjIffGODBEREbksJjJERETkspjIEBERkctiIkNEREQui4lMMy1fvhyDBg1CQEAAwsLCMGrUKJw4ccLkvkIIjBgxAhqNBlu2bDHadurUKYwcORJ+fn4ICwvD008/jRs3bjigBs7F1vOZnZ2NYcOGoU2bNggMDMSQIUNw7do1w/aysjJMmDABgYGBCA4OxrRp0/Dzzz87sipOwZbzWVJSgkmTJiE8PBxt2rTBbbfdho8//thoH57Pm1atWoV+/foZBhBLSEjAjh07DNsrKiowe/ZshIaGwt/fH2PHjsW5c+eMjsHf+02WzmdZWRnmzJmDuLg4tG7dGl26dMEf/vAH6PV6o2PwfN5k7ftZy52uR0xkmmn//v2YPXs2Dh06hF27dqGqqgrDhw/HlStXGuy7cuVKk9MkVFdXY+TIkbh+/ToOHjyINWvWID09HUuXLnVEFZyKLeczOzsbKSkpGD58OL766iscPnwYTzzxhNEQ1hMmTMD333+PXbt2Ydu2bfj8888xffp0GVWSypbz+dvf/hYnTpzA1q1bcezYMYwZMwYPP/wwvvnmG8M+PJ83de7cGStWrMDRo0dx5MgRDBs2DA8++CC+//57AMC8efPw6aefYuPGjdi/fz/Onj2LMWPGGN7P37sxS+fz7NmzOHv2LF599VXk5eUhPT0dGRkZmDZtmuH9PJ/GrH0/a7nV9UiQXZ0/f14AEPv37zda/80334hOnTqJ4uJiAUBs3rzZsG379u2iRYsWoqSkxLBu1apVIjAwUFRWVjqq6E7J1PmMj48Xzz77rNn3HD9+XAAQhw8fNqzbsWOH0Gg04r///a+q5XV2ps5nmzZtxNq1a432CwkJEf/85z+FEDyftmjbtq147733xKVLl4S3t7fYuHGjYdsPP/wgAIjs7GwhBH/vtqg9n6Z8+OGHwsfHR1RVVQkheD5tUf98utv1iHdk7Kz2lmdISIhh3dWrVzF+/Hi8+eabCA8Pb/Ce7Oxs9O3bFx06dDCs02q1KC8vb5BFe5r65/P8+fPIyclBWFgYBg8ejA4dOuCee+7BF198YXhPdnY2goODcfvttxvWJSUloUWLFsjJyXFsBZyMqe/n4MGD8e9//xtlZWWoqanBhg0bUFFRgaFDhwLg+bSkuroaGzZswJUrV5CQkICjR4+iqqoKSUlJhn169uyJLl26IDs7GwB/75bUP5+m6PV6BAYGomVLZapAnk/zTJ1Pd7weuf2kkY5UU1ODuXPn4s4770SfPn0M6+fNm4fBgwfjwQcfNPm+kpISoy8NAMPrkpIS9Qrs5Eydz5MnTwIAnn/+ebz66qsYMGAA1q5di8TEROTl5SEmJgYlJSUICwszOlbLli0REhLC82ni+/nhhx/ikUceQWhoKFq2bAk/Pz9s3rwZ0dHRAMDzacKxY8eQkJCAiooK+Pv7Y/PmzbjllluQm5sLHx8fBAcHG+3foUMHw7ni770hc+ezvgsXLuBPf/qT0WNNns+GLJ1Pd7weMZGxo9mzZyMvL8/o7sDWrVuxZ88eo/YGZBtT57OmpgYAMGPGDDz22GMAgFtvvRW7d+/G6tWrsXz5cilldQWmzicA/PGPf8SlS5eQlZWFdu3aYcuWLXj44Ydx4MAB9O3bV1JpnVtcXBxyc3Oh1+vx0UcfYfLkydi/f7/sYrksc+ezbjJTXl6OkSNH4pZbbsHzzz8vr7AuwNz5LCgocMvrERMZO3niiScMjSA7d+5sWL9nzx4UFhY2+Bfa2LFjcffdd2Pfvn0IDw/HV199ZbS9tpeDqVt/nsDc+YyIiACABv9a69WrF06dOgVAOWfnz5832n7jxg2UlZXxfNY7n4WFhfjHP/6BvLw89O7dGwDQv39/HDhwAG+++Sbefvttnk8TfHx8DHesBg4ciMOHD+Nvf/sbHnnkEVy/fh2XLl0y+s2fO3fOcK74e2/I3Pl85513AACXL19GSkoKAgICsHnzZnh7exvey/PZkLnz2bp1a7e8HrGNTDMJIfDEE09g8+bN2LNnD6Kiooy2L1q0CN999x1yc3MNCwC8/vrrSEtLAwAkJCTg2LFjRheLXbt2ITAw0OTtVXdm7Xx269YNHTt2bNCFWKfToWvXrgCU83np0iUcPXrUsH3Pnj2oqalBfHy8+pVwItbO59WrVwHAqMcXAHh5eRnufvF8WldTU4PKykoMHDgQ3t7e2L17t2HbiRMncOrUKUMbBf7eras9n4ByJ2b48OHw8fHB1q1b0apVK6N9eT6tqz2fbns9ktzY2OXNmjVLBAUFiX379oni4mLDcvXqVbPvQb1W4jdu3BB9+vQRw4cPF7m5uSIjI0O0b99eLF682AE1cC62nM/XX39dBAYGio0bN4r8/Hzx7LPPilatWomCggLDPikpKeLWW28VOTk54osvvhAxMTFi3LhxMqoklbXzef36dREdHS3uvvtukZOTIwoKCsSrr74qNBqN+OyzzwzH4fm8adGiRWL//v2iqKhIfPfdd2LRokVCo9GInTt3CiGEmDlzpujSpYvYs2ePOHLkiEhISBAJCQmG9/P3bszS+dTr9SI+Pl707dtXFBQUGH2Hb9y4IYTg+azP2vezPne4HjGRaSYAJpe0tDSL76n7xRFCiB9//FGMGDFCtG7dWrRr107Mnz/f0L3Qk9h6PpcvXy46d+4s/Pz8REJCgjhw4IDR9tLSUjFu3Djh7+8vAgMDxWOPPSYuX77swJo4B1vOp06nE2PGjBFhYWHCz89P9OvXr0F3bJ7Pm6ZOnSq6du0qfHx8RPv27UViYqLRReLatWvi8ccfF23bthV+fn5i9OjRori42OgY/L3fZOl87t271+x3uKioyHAMns+brH0/63OH65FGCCEcc++HiIiIyL7YRoaIiIhcFhMZIiIicllMZIiIiMhlMZEhIiIil8VEhoiIiFwWExkiIiJyWUxkiIiIyGUxkSEiIiKXxUSGiNzGl19+ib59+8Lb2xujRo2SXRwicgAmMkTUaFOmTIFGo4FGo4G3tzc6dOiA5ORkrF692jDZpD1t27YN99xzDwICAuDn54dBgwYhPT29wX5PPfUUBgwYgKKiIsybNw/e3t744osvjPa5cuUKunfvjgULFti9nETkeExkiKhJUlJSUFxcjB9//BE7duzAvffeiyeffBL3338/bty4Ybc4f//73/Hggw/izjvvRE5ODr777js8+uijmDlzZoNkpLCwEMOGDUPnzp1xzz33YM6cOZgyZQquXLli2GfhwoVo3bo1XnzxRbuVsdb169ftfkwiskL2ZE9E5HomT54sHnzwwQbrd+/eLQCIf/7zn0IIIV577TXRp08f4efnJzp37ixmzZplmGzy559/FgEBAWLjxo1Gx9i8ebPw8/MT5eXl4tSpU8Lb21s89dRTDWK98cYbAoA4dOiQKCoqMjkx5rVr10SvXr3E7NmzhRBC7NmzR/j4+IgjR46I6upq8fLLL4tu3bqJVq1aiX79+hmV5caNG2Lq1KmG7bGxsWLlypUmz8OLL74oIiIiRLdu3Zp1Xomo8XhHhojsZtiwYejfvz82bdoEAGjRogXeeOMNfP/991izZg327NmDhQsXAgDatGmDRx99FGlpaUbHSEtLw0MPPYSAgAB89NFHqKqqMvkYaMaMGfD398f69esRGRmJ4uJiBAYGYuXKlSguLsYjjzyCVq1aYe3atXj33XfxySefYOrUqViyZAkGDhyI5cuXY+3atXj77bfx/fffY968eZg4cSL2798PAKipqUHnzp2xceNGHD9+HEuXLsWSJUvw4YcfGpVj9+7dOHHiBHbt2oVt27apcVqJyBLZmRQRuR5zd2SEEOKRRx4RvXr1Mrlt48aNIjQ01PA6JydHeHl5ibNnzwohhDh37pxo2bKl2LdvnxBCiJkzZ4qgoCCz5ejXr58YMWKE4XVQUJBIS0trsN/SpUtFixYtxMCBA0VVVZWoqKgQfn5+4uDBg0b7TZs2TYwbN85svNmzZ4uxY8caXk+ePFl06NBBVFZWmn0PEamLd2SIyK6EENBoNACArKwsJCYmolOnTggICMCkSZNQWlqKq1evAgB+9atfoXfv3lizZg0A4F//+he6du2KIUOG2LVMf/zjH1FTU4NFixahZcuWKCgowNWrV5GcnAx/f3/DsnbtWhQWFhre9+abb2LgwIFo3749/P398e677+LUqVNGx+7bty98fHzsWl4ish0TGSKyqx9++AFRUVH48ccfcf/996Nfv374+OOPcfToUbz55psAjBvF/u53vzP0QEpLS8Njjz1mSIRiY2Oh1+tx9uzZBnGuX7+OwsJCxMbGWi1Ty5Ytjf77888/AwA+++wz5ObmGpbjx4/jo48+AgBs2LABCxYswLRp07Bz507k5ubisccea9Cgt02bNo05PURkZ0xkiMhu9uzZg2PHjmHs2LE4evQoampq8Nprr+GOO+5AbGysyYRk4sSJ+Omnn/DGG2/g+PHjmDx5smHb2LFj4e3tjddee63B+95++21cuXIF48aNa3Q5b7nlFvj6+uLUqVOIjo42WiIjIwEoY9IMHjwYjz/+OG699VZER0cb3a0hIufQUnYBiMg1VVZWoqSkBNXV1Th37hwyMjKwfPly3H///fjtb3+LvLw8VFVV4e9//zseeOABfPnll3j77bcbHKdt27YYM2YMnn76aQwfPhydO3c2bOvSpQv+8pe/YP78+WjVqhUmTZoEb29vfPLJJ1iyZAnmz5+P+Pj4Rpc9ICAACxYswLx581BTU4O77roLer0eX375JQIDAzF58mTExMRg7dq1yMzMRFRUFN5//30cPnwYUVFRzTpvRGRnshvpEJHrmTx5sqGbc8uWLUX79u1FUlKSWL16taiurjbs99e//lVERESI1q1bC61WK9auXSsAiIsXLxodr7bb9ocffmgy3ieffCLuvvtu0aZNG9GqVSsxcOBAsXr16gb7mWvsK4QQAMTmzZsNr2tqasTKlStFXFyc8Pb2Fu3btxdarVbs379fCCFERUWFmDJliggKChLBwcFi1qxZYtGiRaJ///5G58Fco2cicgyNEEJIzaSIyOO9//77mDdvHs6ePcuGs0TUKHy0RETSXL16FcXFxVixYgVmzJjBJIaIGo2NfYlImr/85S/o2bMnwsPDsXjxYtnFISIXxEdLRERE5LJ4R4aIiIhcFhMZIiIicllMZIiIiMhlMZEhIiIil8VEhoiIiFwWExkiIiJyWUxkiIiIyGUxkSEiIiKX9f9PmheX/r+mHwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax=None\n", + "colors = ['red','blue','green','yellow']\n", + "for i,var in enumerate(new_pumpkins['Variety'].unique()):\n", + " df = new_pumpkins[new_pumpkins['Variety']==var]\n", + " ax = df.plot.scatter('DayOfYear','Price',ax=ax,c=colors[i],label=var)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pie_pumpkins = new_pumpkins[new_pumpkins['Variety']=='PIE TYPE']\n", + "pie_pumpkins.plot.scatter('DayOfYear','Price') " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 144 entries, 70 to 1630\n", + "Data columns (total 8 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Month 144 non-null int32 \n", + " 1 DayOfYear 144 non-null int64 \n", + " 2 Variety 144 non-null object \n", + " 3 City 144 non-null object \n", + " 4 Package 144 non-null object \n", + " 5 Low Price 144 non-null float64\n", + " 6 High Price 144 non-null float64\n", + " 7 Price 144 non-null float64\n", + "dtypes: float64(3), int32(1), int64(1), object(3)\n", + "memory usage: 9.6+ KB\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\billg\\AppData\\Local\\Temp\\ipykernel_5488\\3144308612.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " pie_pumpkins.dropna(inplace=True)\n" + ] + } + ], + "source": [ + "pie_pumpkins.dropna(inplace=True)\n", + "pie_pumpkins.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import mean_squared_error\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "X = pie_pumpkins['DayOfYear'].to_numpy().reshape(-1,1)\n", + "y = pie_pumpkins['Price']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lin_reg = LinearRegression()\n", + "lin_reg.fit(X_train,y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean error: 2.77 (17.2%)\n" + ] + } + ], + "source": [ + "pred = lin_reg.predict(X_test)\n", + "\n", + "mse = np.sqrt(mean_squared_error(y_test,pred))\n", + "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model determination: 0.04460606335028361\n" + ] + } + ], + "source": [ + "score = lin_reg.score(X_train,y_train)\n", + "print('Model determination: ', score)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X_test,y_test)\n", + "plt.plot(X_test,pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures()),\n", + " ('linearregression', LinearRegression())])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.preprocessing import PolynomialFeatures\n", + "from sklearn.pipeline import make_pipeline\n", + "\n", + "pipeline = make_pipeline(PolynomialFeatures(2), LinearRegression())\n", + "\n", + "pipeline.fit(X_train,y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " FAIRYTALE MINIATURE MIXED HEIRLOOM VARIETIES PIE TYPE\n", + "70 False False False True\n", + "71 False False False True\n", + "72 False False False True\n", + "73 False False False True\n", + "74 False False False True\n", + "... ... ... ... ...\n", + "1738 False True False False\n", + "1739 False True False False\n", + "1740 False True False False\n", + "1741 False True False False\n", + "1742 False True False False\n", + "\n", + "[415 rows x 4 columns]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.get_dummies(new_pumpkins['Variety'])" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "X = pd.get_dummies(new_pumpkins['Variety'])\n", + "y = new_pumpkins['Price']" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "X = pd.get_dummies(new_pumpkins['Variety']) \\\n", + " .join(new_pumpkins['Month']) \\\n", + " .join(pd.get_dummies(new_pumpkins['City'])) \\\n", + " .join(pd.get_dummies(new_pumpkins['Package']))\n", + "y = new_pumpkins['Price']" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean error: 2.23 (8.26%)\n", + "Model determination: 0.9643539089222867\n" + ] + } + ], + "source": [ + "# make train-test split\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n", + "\n", + "# setup and train the pipeline\n", + "pipeline = make_pipeline(PolynomialFeatures(2), LinearRegression())\n", + "pipeline.fit(X_train,y_train)\n", + "\n", + "# predict results for test data\n", + "pred = pipeline.predict(X_test)\n", + "\n", + "# calculate MSE and determination\n", + "mse = np.sqrt(mean_squared_error(y_test,pred))\n", + "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n", + "\n", + "score = pipeline.score(X_train,y_train)\n", + "print('Model determination: ', score)" + ] } ], "metadata": { @@ -106,7 +1799,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3-final" + "version": "3.12.5" }, "orig_nbformat": 2 }, diff --git a/2-Regression/4-Logistic/assignment.ipynb b/2-Regression/4-Logistic/assignment.ipynb new file mode 100644 index 000000000..803b40812 --- /dev/null +++ b/2-Regression/4-Logistic/assignment.ipynb @@ -0,0 +1,620 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 274 entries, 0 to 273\n", + "Data columns (total 7 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 species 274 non-null object \n", + " 1 island 274 non-null object \n", + " 2 bill_length_mm 274 non-null float64\n", + " 3 bill_depth_mm 274 non-null float64\n", + " 4 flipper_length_mm 274 non-null float64\n", + " 5 body_mass_g 274 non-null float64\n", + " 6 year 274 non-null int64 \n", + "dtypes: float64(4), int64(1), object(2)\n", + "memory usage: 15.1+ KB\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "full_penguins = pd.read_csv('penguins_binary_classification.csv')\n", + "\n", + "full_penguins.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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speciesislandbill_length_mmbill_depth_mmflipper_length_mmbody_mass_gyear
0AdelieTorgersen39.118.7181.03750.02007
1AdelieTorgersen39.517.4186.03800.02007
2AdelieTorgersen40.318.0195.03250.02007
3AdelieTorgersen36.719.3193.03450.02007
4AdelieTorgersen39.320.6190.03650.02007
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" + ], + "text/plain": [ + " species island bill_length_mm bill_depth_mm flipper_length_mm \\\n", + "0 Adelie Torgersen 39.1 18.7 181.0 \n", + "1 Adelie Torgersen 39.5 17.4 186.0 \n", + "2 Adelie Torgersen 40.3 18.0 195.0 \n", + "3 Adelie Torgersen 36.7 19.3 193.0 \n", + "4 Adelie Torgersen 39.3 20.6 190.0 \n", + "\n", + " body_mass_g year \n", + "0 3750.0 2007 \n", + "1 3800.0 2007 \n", + "2 3250.0 2007 \n", + "3 3450.0 2007 \n", + "4 3650.0 2007 " + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "full_penguins.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "full_penguins.species.unique()\n", + "\n", + "pallete = {\n", + " \"Adelie\" :\"black\",\n", + " \"Gentoo\" : \"orange\"\n", + "}\n", + "\n", + "import seaborn as sns\n", + "\n", + "sns.catplot(data=full_penguins, y=\"island\", hue=\"species\", kind=\"count\", palette=pallete)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import OneHotEncoder\n", + "\n", + "categorical_features = [\"island\"]\n", + "categorical_encoder = OneHotEncoder(sparse_output=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import OrdinalEncoder\n", + "\n", + "ordinal_features = [\"bill_length_mm\",\t\"bill_depth_mm\",\t\"flipper_length_mm\",\t\"body_mass_g\",\t\"year\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cat__island_Biscoecat__island_Dreamcat__island_Torgersen
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" + ], + "text/plain": [ + " cat__island_Biscoe cat__island_Dream cat__island_Torgersen\n", + "0 0.0 0.0 1.0\n", + "1 0.0 0.0 1.0\n", + "2 0.0 0.0 1.0\n", + "3 0.0 0.0 1.0\n", + "4 0.0 0.0 1.0" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.compose import ColumnTransformer\n", + "ct = ColumnTransformer(transformers=[(\"cat\", categorical_encoder, categorical_features)])\n", + "\n", + "ct.set_output(transform=\"pandas\")\n", + "encoded_features = ct.fit_transform(full_penguins)\n", + "encoded_features.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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bill_length_mmbill_depth_mmflipper_length_mmbody_mass_gyearcat__island_Biscoecat__island_Dreamcat__island_Torgersenspecies
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" + ], + "text/plain": [ + " bill_length_mm bill_depth_mm flipper_length_mm body_mass_g year \\\n", + "0 39.1 18.7 181.0 3750.0 2007 \n", + "1 39.5 17.4 186.0 3800.0 2007 \n", + "2 40.3 18.0 195.0 3250.0 2007 \n", + "3 36.7 19.3 193.0 3450.0 2007 \n", + "4 39.3 20.6 190.0 3650.0 2007 \n", + "\n", + " cat__island_Biscoe cat__island_Dream cat__island_Torgersen species \n", + "0 0.0 0.0 1.0 0 \n", + "1 0.0 0.0 1.0 0 \n", + "2 0.0 0.0 1.0 0 \n", + "3 0.0 0.0 1.0 0 \n", + "4 0.0 0.0 1.0 0 " + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.preprocessing import LabelEncoder\n", + "\n", + "label_encoder = LabelEncoder()\n", + "encoded_label = label_encoder.fit_transform(full_penguins.species)\n", + "encoded_penguins = encoded_features.assign(species=encoded_label)\n", + "encoded_penguins.head()\n", + "\n", + "full_penguins.head()\n", + "\n", + "columns_for_training = [\"bill_length_mm\", \"bill_depth_mm\", \"flipper_length_mm\", \"body_mass_g\", \"year\"]\n", + "\n", + "encoded_penguins = pd.concat([full_penguins[columns_for_training],encoded_penguins], axis=1)\n", + "\n", + "encoded_penguins.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Adelie', 'Gentoo']" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(label_encoder.inverse_transform([0,1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9017964035172064\n", + "-0.8326064366975109\n", + "0.9017964035172064\n", + "0.8198367781987648\n" + ] + } + ], + "source": [ + "print(encoded_penguins.flipper_length_mm.corr(encoded_penguins.species))\n", + "print(encoded_penguins.bill_depth_mm.corr(encoded_penguins.species))\n", + "print(encoded_penguins.flipper_length_mm.corr(encoded_penguins.species))\n", + "print(encoded_penguins.body_mass_g.corr(encoded_penguins.species))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "X = encoded_penguins[encoded_penguins.columns.difference([\"species\"])]\n", + "\n", + "y = encoded_penguins.species\n", + "\n", + "XTrain, XTest, yTrain, yTest = train_test_split(X, y, test_size=0.2, random_state=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import f1_score, classification_report\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "model = LogisticRegression()\n", + "model.fit(XTrain, yTrain)\n", + "preds = model.predict(XTest)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 28\n", + " 1 1.00 1.00 1.00 27\n", + "\n", + " accuracy 1.00 55\n", + " macro avg 1.00 1.00 1.00 55\n", + "weighted avg 1.00 1.00 1.00 55\n", + "\n", + "Predicted labels: [1 1 1 0 1 1 0 1 0 1 1 1 1 0 0 0 0 1 0 1 0 0 1 1 0 0 0 1 1 0 0 0 0 1 0 0 1\n", + " 0 0 1 0 1 1 0 0 1 1 1 1 0 1 0 0 0 1]\n", + "F1-score: 1.0\n" + ] + } + ], + "source": [ + "print(classification_report(yTest, preds))\n", + "print(\"Predicted labels: \", preds)\n", + "print(\"F1-score: \", f1_score(yTest, preds))" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import roc_curve, roc_auc_score\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "y_scores = model.predict_proba(XTest)\n", + "fpr, tpr, thresholds = roc_curve(yTest, y_scores[:,1])\n", + "\n", + "fig = plt.figure(figsize=(6, 6))\n", + "plt.plot([0, 1], [0, 1], 'k--')\n", + "plt.plot(fpr, tpr)\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('ROC Curve')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0\n" + ] + } + ], + "source": [ + "auc = roc_auc_score(yTest,y_scores[:,1])\n", + "print(auc)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/2-Regression/4-Logistic/notebook.ipynb b/2-Regression/4-Logistic/notebook.ipynb index 7c2127636..a4eba9951 100644 --- a/2-Regression/4-Logistic/notebook.ipynb +++ b/2-Regression/4-Logistic/notebook.ipynb @@ -224,6 +224,939 @@ "\n", "full_pumpkins.head()\n" ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "columns_to_select = [\"City Name\", \"Package\", \"Variety\",\"Origin\",\"Item Size\", \"Color\"]\n", + "pumpkins = full_pumpkins.loc[:,columns_to_select]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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City NamePackageVarietyOriginItem SizeColor
2BALTIMORE24 inch binsHOWDEN TYPEDELAWAREmedORANGE
3BALTIMORE24 inch binsHOWDEN TYPEVIRGINIAmedORANGE
4BALTIMORE24 inch binsHOWDEN TYPEMARYLANDlgeORANGE
5BALTIMORE24 inch binsHOWDEN TYPEMARYLANDlgeORANGE
6BALTIMORE36 inch binsHOWDEN TYPEMARYLANDmedORANGE
\n", + "
" + ], + "text/plain": [ + " City Name Package Variety Origin Item Size Color\n", + "2 BALTIMORE 24 inch bins HOWDEN TYPE DELAWARE med ORANGE\n", + "3 BALTIMORE 24 inch bins HOWDEN TYPE VIRGINIA med ORANGE\n", + "4 BALTIMORE 24 inch bins HOWDEN TYPE MARYLAND lge ORANGE\n", + "5 BALTIMORE 24 inch bins HOWDEN TYPE MARYLAND lge ORANGE\n", + "6 BALTIMORE 36 inch bins HOWDEN TYPE MARYLAND med ORANGE" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pumpkins.dropna(inplace=True)\n", + "pumpkins.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "\n", + "palette = {\n", + " \"ORANGE\": \"orange\",\n", + " \"WHITE\": \"wheat\"\n", + "}\n", + "\n", + "sns.catplot(data=pumpkins, y=\"Variety\", hue=\"Color\", kind=\"count\", palette=palette)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['med', 'lge', 'sml', 'xlge', 'med-lge', 'jbo', 'exjbo'],\n", + " dtype=object)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pumpkins[\"Item Size\"].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import OrdinalEncoder\n", + "\n", + "item_size_categories = [['sml','med', 'med-lge', 'lge', 'xlge', 'jbo','exjbo']]\n", + "ordinal_features = ['Item Size']\n", + "ordinal_encoder = OrdinalEncoder(categories=item_size_categories)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import OneHotEncoder\n", + "\n", + "categorical_features=[\"City Name\", \"Package\", \"Variety\", \"Origin\"]\n", + "categorical_encoder = OneHotEncoder(sparse_output=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " ord__Item Size cat__City Name_ATLANTA cat__City Name_BALTIMORE \\\n", + "1694 4.0 0.0 0.0 \n", + "1695 4.0 0.0 0.0 \n", + "1696 4.0 0.0 0.0 \n", + "1697 4.0 0.0 0.0 \n", + "1698 4.0 0.0 0.0 \n", + "\n", + " cat__City Name_BOSTON cat__City Name_CHICAGO cat__City Name_COLUMBIA \\\n", + "1694 0.0 0.0 0.0 \n", + "1695 0.0 0.0 0.0 \n", + "1696 0.0 0.0 0.0 \n", + "1697 0.0 0.0 0.0 \n", + "1698 0.0 0.0 0.0 \n", + "\n", + " cat__City Name_DALLAS cat__City Name_DETROIT \\\n", + "1694 0.0 0.0 \n", + "1695 0.0 0.0 \n", + "1696 0.0 0.0 \n", + "1697 0.0 0.0 \n", + "1698 0.0 0.0 \n", + "\n", + " cat__City Name_LOS ANGELES cat__City Name_MIAMI ... \\\n", + "1694 0.0 0.0 ... \n", + "1695 0.0 0.0 ... \n", + "1696 0.0 0.0 ... \n", + "1697 0.0 0.0 ... \n", + "1698 0.0 0.0 ... \n", + "\n", + " cat__Origin_NEW JERSEY cat__Origin_NEW YORK \\\n", + "1694 0.0 0.0 \n", + "1695 0.0 0.0 \n", + "1696 0.0 0.0 \n", + "1697 0.0 0.0 \n", + "1698 0.0 0.0 \n", + "\n", + " cat__Origin_NORTH CAROLINA cat__Origin_OHIO cat__Origin_PENNSYLVANIA \\\n", + "1694 0.0 0.0 0.0 \n", + "1695 0.0 0.0 0.0 \n", + "1696 0.0 0.0 0.0 \n", + "1697 0.0 0.0 0.0 \n", + "1698 0.0 0.0 0.0 \n", + "\n", + " cat__Origin_TENNESSEE cat__Origin_TEXAS cat__Origin_VERMONT \\\n", + "1694 0.0 0.0 0.0 \n", + "1695 0.0 0.0 0.0 \n", + "1696 0.0 0.0 0.0 \n", + "1697 0.0 0.0 0.0 \n", + "1698 0.0 0.0 0.0 \n", + "\n", + " cat__Origin_VIRGINIA Color \n", + "1694 0.0 1 \n", + "1695 0.0 1 \n", + "1696 0.0 1 \n", + "1697 0.0 1 \n", + "1698 0.0 1 \n", + "\n", + "[5 rows x 49 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.preprocessing import LabelEncoder\n", + "\n", + "label_encoder = LabelEncoder()\n", + "encoded_label = label_encoder.fit_transform(pumpkins[\"Color\"])\n", + "encoded_pumpkins = encoded_features.assign(Color=encoded_label)\n", + "encoded_pumpkins.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['ORANGE', 'WHITE']" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(label_encoder.inverse_transform([0,1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\billg\\AppData\\Local\\Temp\\ipykernel_16772\\2446460816.py:3: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " g = sns.catplot(data=pumpkins, x=\"Item Size\", y=\"Color\", row=\"Variety\",\n", + "c:\\Users\\billg\\Desktop\\alvinsstuff\\Learning\\ML-For-Beginners\\.venv\\Lib\\site-packages\\seaborn\\axisgrid.py:123: UserWarning: Tight layout not applied. tight_layout cannot make Axes height small enough to accommodate all Axes decorations.\n", + " self._figure.tight_layout(*args, **kwargs)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pumpkins['Item Size'] = encoded_pumpkins['ord__Item Size']\n", + "\n", + "g = sns.catplot(data=pumpkins, x=\"Item Size\", y=\"Color\", row=\"Variety\",\n", + " kind=\"box\", orient=\"h\",\n", + " sharex=False, margin_titles=True,\n", + " height=1.5,aspect=4,palette=palette)\n", + "\n", + "g.set(xlabel=\"Item Size\", ylabel=\"\").set(xlim=(0,6))\n", + "g.set_titles(row_template=\"{row_name}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\billg\\AppData\\Local\\Temp\\ipykernel_16772\\1781995252.py:5: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " sns.swarmplot(x=\"Color\", y=\"ord__Item Size\", data=encoded_pumpkins, palette=palette)\n", + "c:\\Users\\billg\\Desktop\\alvinsstuff\\Learning\\ML-For-Beginners\\.venv\\Lib\\site-packages\\seaborn\\categorical.py:3399: UserWarning: 63.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n", + " warnings.warn(msg, UserWarning)\n", + "c:\\Users\\billg\\Desktop\\alvinsstuff\\Learning\\ML-For-Beginners\\.venv\\Lib\\site-packages\\seaborn\\categorical.py:3399: UserWarning: 21.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n", + " warnings.warn(msg, UserWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\billg\\Desktop\\alvinsstuff\\Learning\\ML-For-Beginners\\.venv\\Lib\\site-packages\\seaborn\\categorical.py:3399: UserWarning: 79.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n", + " warnings.warn(msg, UserWarning)\n", + "c:\\Users\\billg\\Desktop\\alvinsstuff\\Learning\\ML-For-Beginners\\.venv\\Lib\\site-packages\\seaborn\\categorical.py:3399: UserWarning: 35.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n", + " warnings.warn(msg, UserWarning)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "palette = {\n", + " \"0\": 'orange',\n", + " \"1\": 'wheat'\n", + " }\n", + "sns.swarmplot(x=\"Color\", y=\"ord__Item Size\", data=encoded_pumpkins, palette=palette)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "X = encoded_pumpkins[encoded_pumpkins.columns.difference([\"Color\"])]\n", + "\n", + "y = encoded_pumpkins[\"Color\"]\n", + "\n", + "XTrain, XTest, yTrain, yTest = train_test_split(X, y, test_size=0.2, random_state=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import f1_score, classification_report\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "model = LogisticRegression()\n", + "model.fit(XTrain, yTrain)\n", + "preds = model.predict(XTest)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.94 0.98 0.96 166\n", + " 1 0.85 0.67 0.75 33\n", + "\n", + " accuracy 0.92 199\n", + " macro avg 0.89 0.82 0.85 199\n", + "weighted avg 0.92 0.92 0.92 199\n", + "\n", + "Predicted labels: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0\n", + " 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0\n", + " 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1\n", + " 0 0 0 1 0 0 0 0 0 0 0 0 1 1]\n", + "F1-score: 0.7457627118644068\n" + ] + } + ], + "source": [ + "print(classification_report(yTest, preds))\n", + "print(\"Predicted labels: \", preds)\n", + "print(\"F1-score: \", f1_score(yTest, preds))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import roc_curve, roc_auc_score\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "y_scores = model.predict_proba(XTest)\n", + "fpr, tpr, thresholds = roc_curve(yTest, y_scores[:,1])\n", + "\n", + "fig = plt.figure(figsize=(6, 6))\n", + "plt.plot([0, 1], [0, 1], 'k--')\n", + "plt.plot(fpr, tpr)\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('ROC Curve')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9749908725812341\n" + ] + } + ], + "source": [ + "auc = roc_auc_score(yTest,y_scores[:,1])\n", + "print(auc)" + ] } ], "metadata": { @@ -242,7 +1175,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.1" + "version": "3.12.5" }, "metadata": { "interpreter": { diff --git a/2-Regression/4-Logistic/penguins_binary_classification.csv b/2-Regression/4-Logistic/penguins_binary_classification.csv new file mode 100644 index 000000000..57fdab32d --- /dev/null +++ b/2-Regression/4-Logistic/penguins_binary_classification.csv @@ -0,0 +1,275 @@ +species,island,bill_length_mm,bill_depth_mm,flipper_length_mm,body_mass_g,year +Adelie,Torgersen,39.1,18.7,181.0,3750.0,2007 +Adelie,Torgersen,39.5,17.4,186.0,3800.0,2007 +Adelie,Torgersen,40.3,18.0,195.0,3250.0,2007 +Adelie,Torgersen,36.7,19.3,193.0,3450.0,2007 +Adelie,Torgersen,39.3,20.6,190.0,3650.0,2007 +Adelie,Torgersen,38.9,17.8,181.0,3625.0,2007 +Adelie,Torgersen,39.2,19.6,195.0,4675.0,2007 +Adelie,Torgersen,34.1,18.1,193.0,3475.0,2007 +Adelie,Torgersen,42.0,20.2,190.0,4250.0,2007 +Adelie,Torgersen,37.8,17.1,186.0,3300.0,2007 +Adelie,Torgersen,37.8,17.3,180.0,3700.0,2007 +Adelie,Torgersen,41.1,17.6,182.0,3200.0,2007 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datetimecitystatecountryshapeduration (seconds)duration (hours/min)commentsdate postedlatitudelongitude
010/10/1949 20:30san marcostxuscylinder2700.045 minutesThis event took place in early fall around 194...4/27/200429.883056-97.941111
110/10/1949 21:00lackland afbtxNaNlight7200.01-2 hrs1949 Lackland AFB&#44 TX. Lights racing acros...12/16/200529.384210-98.581082
210/10/1955 17:00chester (uk/england)NaNgbcircle20.020 secondsGreen/Orange circular disc over Chester&#44 En...1/21/200853.200000-2.916667
310/10/1956 21:00ednatxuscircle20.01/2 hourMy older brother and twin sister were leaving ...1/17/200428.978333-96.645833
410/10/1960 20:00kaneohehiuslight900.015 minutesAS a Marine 1st Lt. flying an FJ4B fighter/att...1/22/200421.418056-157.803611
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" + ], + "text/plain": [ + " datetime city state country shape \\\n", + "0 10/10/1949 20:30 san marcos tx us cylinder \n", + "1 10/10/1949 21:00 lackland afb tx NaN light \n", + "2 10/10/1955 17:00 chester (uk/england) NaN gb circle \n", + "3 10/10/1956 21:00 edna tx us circle \n", + "4 10/10/1960 20:00 kaneohe hi us light \n", + "\n", + " duration (seconds) duration (hours/min) \\\n", + "0 2700.0 45 minutes \n", + "1 7200.0 1-2 hrs \n", + "2 20.0 20 seconds \n", + "3 20.0 1/2 hour \n", + "4 900.0 15 minutes \n", + "\n", + " comments date posted latitude \\\n", + "0 This event took place in early fall around 194... 4/27/2004 29.883056 \n", + "1 1949 Lackland AFB, TX. Lights racing acros... 12/16/2005 29.384210 \n", + "2 Green/Orange circular disc over Chester, En... 1/21/2008 53.200000 \n", + "3 My older brother and twin sister were leaving ... 1/17/2004 28.978333 \n", + "4 AS a Marine 1st Lt. flying an FJ4B fighter/att... 1/22/2004 21.418056 \n", + "\n", + " longitude \n", + "0 -97.941111 \n", + "1 -98.581082 \n", + "2 -2.916667 \n", + "3 -96.645833 \n", + "4 -157.803611 " + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "ufos =pd.read_csv(\"./data/ufos.csv\")\n", + "ufos.head()\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['us', nan, 'gb', 'ca', 'au', 'de'], dtype=object)" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ufos = pd.DataFrame({'Seconds': ufos['duration (seconds)'], 'Country': ufos['country'],'Latitude': ufos['latitude'],'Longitude': ufos['longitude']})\n", + " \n", + "ufos.Country.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 25863 entries, 2 to 80330\n", + "Data columns (total 4 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Seconds 25863 non-null float64\n", + " 1 Country 25863 non-null object \n", + " 2 Latitude 25863 non-null float64\n", + " 3 Longitude 25863 non-null float64\n", + "dtypes: float64(3), object(1)\n", + "memory usage: 1010.3+ KB\n" + ] + } + ], + "source": [ + "ufos.dropna(inplace=True)\n", + " \n", + "ufos = ufos[(ufos['Seconds'] >= 1) & (ufos['Seconds'] <= 60)]\n", + " \n", + "ufos.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 1, 2, 3, 4]\n", + "['au' 'ca' 'de' 'gb' 'us']\n" + ] + } + ], + "source": [ + "from sklearn.preprocessing import LabelEncoder\n", + "labelencoder = LabelEncoder()\n", + "ufos['Country'] = labelencoder.fit_transform(ufos['Country'])\n", + " \n", + "ufos.head()\n", + "countries = [0,1,2,3,4]\n", + "print(countries)\n", + "print(labelencoder.inverse_transform(countries))" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "selected_features = [\"Seconds\", \"Latitude\", \"Longitude\"]\n", + "\n", + "X = ufos[selected_features]\n", + "y= ufos[\"Country\"]\n", + "\n", + "XTrain, XTest, yTrain, yTest = train_test_split(X, y, test_size=0.2, random_state=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 41\n", + " 1 0.83 0.23 0.36 250\n", + " 2 1.00 1.00 1.00 8\n", + " 3 1.00 1.00 1.00 131\n", + " 4 0.96 1.00 0.98 4743\n", + "\n", + " accuracy 0.96 5173\n", + " macro avg 0.96 0.85 0.87 5173\n", + "weighted avg 0.96 0.96 0.95 5173\n", + "\n", + "Predicted labels: [4 4 4 ... 3 4 4]\n", + "Accuracy: 0.9605644693601392\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\billg\\Desktop\\alvinsstuff\\Learning\\ML-For-Beginners\\.venv\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n" + ] + } + ], + "source": [ + "from sklearn.metrics import accuracy_score, classification_report\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "model = LogisticRegression()\n", + "model.fit(XTrain, yTrain)\n", + "\n", + "preds = model.predict(XTest)\n", + "\n", + "print(classification_report(yTest, preds))\n", + "print('Predicted labels: ', preds)\n", + "print('Accuracy: ', accuracy_score(yTest, preds))" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "\n", + "model_filename = \"ufo-model.pkl\"\n", + "pickle.dump(model, open(model_filename, \"wb\"))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\billg\\Desktop\\alvinsstuff\\Learning\\ML-For-Beginners\\.venv\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "model = pickle.load(open(model_filename, \"rb\"))\n", + "\n", + "print(model.predict([[50,44,-12]]))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/3-Web-App/1-Web-App/ufo-model.pkl b/3-Web-App/1-Web-App/ufo-model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..7b492f405f35b9ec976e6de76d33ce4b96abf715 GIT binary patch literal 1011 zcmZ`&OK1~87|x^Hq-iTQh(aG)QLs6PfMML9NMPoZk=`vP%r0NO9B#H#o-)VSzd1HY7aJma%wD|FoAkgVjof%EC@2KOl4G} zAPWPiPsV8jORP`^>S0we-NYa`sIONvEJ%Ye)#VyGs>e(Q?p1@x5Q(C*xF(K8k})vS z#1uA}as+gxmPw_vJd$(+^A?q87$WOJ6=FQPW)dSK5!NQkStl8*CYO$kFy29kIAd@c zx}ZsL3hqHj)=^F-*)%SexOTR|aIW;%O`ZJ^jYd0uvSodQ5P{h6kz()n6shE*^-iXh zWMmAqqEpurr5Lk=beV>1{Uzywn|iITFe{L(O*RzOB-i}b!m(sMbt#J!wEv^PQB)9V zD&42U;j{ynz1x%JD6?}Y>hNgR-7LlFv^iTATqoNWdoD4S$qdm#<0Ku&m%`!%_mJfv z{+ENL(fWoy_7Cv0UqY9+UClM_t}OUk>aGv%SQq~OXmrKFGjpL$qZ8jelU!l{mhSPV z?SaDBcs_9JzI*&h uqI + + + + 🛸 UFO Appearance Prediction! 👽 + + + + +
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According to the number of seconds, latitude and longitude, which country is likely to have reported seeing a UFO?

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{{ prediction_text }}

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+ + + \ No newline at end of file diff --git a/3-Web-App/1-Web-App/web-app/ufo-model.pkl b/3-Web-App/1-Web-App/web-app/ufo-model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..7b492f405f35b9ec976e6de76d33ce4b96abf715 GIT binary patch literal 1011 zcmZ`&OK1~87|x^Hq-iTQh(aG)QLs6PfMML9NMPoZk=`vP%r0NO9B#H#o-)VSzd1HY7aJma%wD|FoAkgVjof%EC@2KOl4G} zAPWPiPsV8jORP`^>S0we-NYa`sIONvEJ%Ye)#VyGs>e(Q?p1@x5Q(C*xF(K8k})vS z#1uA}as+gxmPw_vJd$(+^A?q87$WOJ6=FQPW)dSK5!NQkStl8*CYO$kFy29kIAd@c zx}ZsL3hqHj)=^F-*)%SexOTR|aIW;%O`ZJ^jYd0uvSodQ5P{h6kz()n6shE*^-iXh zWMmAqqEpurr5Lk=beV>1{Uzywn|iITFe{L(O*RzOB-i}b!m(sMbt#J!wEv^PQB)9V zD&42U;j{ynz1x%JD6?}Y>hNgR-7LlFv^iTATqoNWdoD4S$qdm#<0Ku&m%`!%_mJfv z{+ENL(fWoy_7Cv0UqY9+UClM_t}OUk>aGv%SQq~OXmrKFGjpL$qZ8jelU!l{mhSPV z?SaDBcs_9JzI*&h uqI\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Unnamed: 0cuisinealmondangelicaaniseanise_seedappleapple_brandyapricotarmagnac...whiskeywhite_breadwhite_winewhole_grain_wheat_flourwinewoodyamyeastyogurtzucchini
065indian00000000...0000000000
166indian10000000...0000000000
267indian00000000...0000000000
368indian00000000...0000000000
469indian00000000...0000000010
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5 rows × 385 columns

\n", + "" + ], + "text/plain": [ + " Unnamed: 0 cuisine almond angelica anise anise_seed apple \\\n", + "0 65 indian 0 0 0 0 0 \n", + "1 66 indian 1 0 0 0 0 \n", + "2 67 indian 0 0 0 0 0 \n", + "3 68 indian 0 0 0 0 0 \n", + "4 69 indian 0 0 0 0 0 \n", + "\n", + " apple_brandy apricot armagnac ... whiskey white_bread white_wine \\\n", + "0 0 0 0 ... 0 0 0 \n", + "1 0 0 0 ... 0 0 0 \n", + "2 0 0 0 ... 0 0 0 \n", + "3 0 0 0 ... 0 0 0 \n", + "4 0 0 0 ... 0 0 0 \n", + "\n", + " whole_grain_wheat_flour wine wood yam yeast yogurt zucchini \n", + "0 0 0 0 0 0 0 0 \n", + "1 0 0 0 0 0 0 0 \n", + "2 0 0 0 0 0 0 0 \n", + "3 0 0 0 0 0 0 0 \n", + "4 0 0 0 0 0 1 0 \n", + "\n", + "[5 rows x 385 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 2448 entries, 0 to 2447\n", + "Columns: 385 entries, Unnamed: 0 to zucchini\n", + "dtypes: int64(384), object(1)\n", + "memory usage: 7.2+ MB\n" + ] + } + ], + "source": [ + "\n", + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df.cuisine.value_counts().plot.barh()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "thai df: (289, 385)\n", + "japanese df: (320, 385)\n", + "chinese df: (442, 385)\n", + "indian df: (598, 385)\n", + "korean df: (799, 385)\n" + ] + } + ], + "source": [ + "thai_df = df[(df.cuisine == \"thai\")]\n", + "japanese_df = df[(df.cuisine == \"japanese\")]\n", + "chinese_df = df[(df.cuisine == \"chinese\")]\n", + "indian_df = df[(df.cuisine == \"indian\")]\n", + "korean_df = df[(df.cuisine == \"korean\")]\n", + " \n", + "print(f'thai df: {thai_df.shape}')\n", + "print(f'japanese df: {japanese_df.shape}')\n", + "print(f'chinese df: {chinese_df.shape}')\n", + "print(f'indian df: {indian_df.shape}')\n", + "print(f'korean df: {korean_df.shape}')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def create_ingredient_df(df):\n", + " ingredient_df = df.T.drop([\"cuisine\", \"Unnamed: 0\"]).sum(axis=1).to_frame(\"value\")\n", + " ingredient_df = ingredient_df[(ingredient_df.T != 0).any()]\n", + " ingredient_df = ingredient_df.sort_values(by='value', ascending=False,\n", + " inplace=False)\n", + " return ingredient_df" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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cnKzFixfr1Vdf1Z49e/Tkk0/qr3/9qzZs2GDX7qmnnlJKSooyMzMVERFRod7i4mIVFhbavQAAQPVlymtrx48f19y5czV//nzFxMRIkho3bqwuXbro9ddf16lTp7R48WLb/Wbz589X//799fzzz8vf31+S5OHhoTfeeEMuLi7n3E9sbKzt59DQUD377LMaNWqUXn75Zdvy0tJSvfrqq2rcuLEkacyYMZo6dapt/Zw5cxQfH69BgwZJkl599VWtWbPGtr64uFjPPfec1q5dq06dOkmSwsLCtGnTJr322mvq1q2bre3UqVPVu3fvc9abnJyspKSk8w8eAACoNkwZ1DIzM1VcXKyePXuedV2rVq3sHgro3LmzysvLlZWVZQtqLVu2PG9Ik6S1a9cqOTlZ+/btU2FhoU6fPq1Tp07p5MmTcnf//Zv+3d3dbSFNkgIDA3X06FFJUkFBgfLy8tSxY0fb+tq1a6t9+/a2y5/79+/XyZMnKwSwkpIStWnTxm5Z+/btz1tvfHy8xo8fb3tfWFio4ODg824DAACqLlMGNTc3tyvu40JPd+bk5OjOO+/UY489punTp8vPz0+bNm3SiBEjVFJSYgtqzs7OdttZLJYK96Cdz5l73lavXq0GDRrYrbNarZdUs9VqrbANAACovkx5j1p4eLjc3NyUlpZWYV3z5s21c+dOnThxwrYsPT1dTk5OtocGLsa3336r8vJyzZw5UzfffLNuuOEGHT58+JLq9PHxUWBgoLZu3Wpbdvr0aX377be29zfeeKOsVqtyc3PVpEkTuxezYQAA4HxMOaPm6uqquLg4TZo0SS4uLurcubN++eUX7dmzR9HR0UpMTFRMTIymTJmiX375RWPHjtUDDzxgu+x5MZo0aaLS0lK99NJL6t+/v9LT0/Xqq69ecq1PPPGEUlJSFB4ermbNmmnWrFnKz8+3rffy8tLEiRP15JNPqry8XF26dFFBQYHS09Pl7e1tuwcPAADgz0wZ1CQpISFBtWvX1uTJk3X48GEFBgZq1KhRcnd315o1a/TEE08oMjJS7u7uuueeezRr1qxL6r9Vq1aaNWuWnn/+ecXHx6tr165KTk7WsGHDLqmfCRMmKC8vTzExMXJyctJDDz2ku+++WwUFBbY206ZNU7169ZScnKwDBw7I19dXbdu21d///vdL2hcAAKhZLMal3HAFUyksLJSPj4+CY1fIyeru6HIA1CA5Kf0cXQJQZZ35/C4oKJC3t/d525ryHjUAAAAQ1AAAAEzLtPeo4eLtToq64NQpAACoephRAwAAMCmCGgAAgEkR1AAAAEyKoAYAAGBSBDUAAACTIqgBAACYFEENAADApAhqAAAAJkVQAwAAMCmCGgAAgEkR1AAAAEyKoAYAAGBSBDUAAACTIqgBAACYFEENAADApAhqAAAAJkVQAwAAMCmCGgAAgEnVdnQBuHItEtfIyeru6DIA1FA5Kf0cXQJQbTGjBgAAYFIENQAAAJMiqAEAAJhUjQlqU6ZMUevWrc/bZvjw4Ro4cOA1qUeSunfvrtjYWNv70NBQzZkz55rtHwAAmFuVe5hg+PDhys/P18qVKx1dyhX74IMP5Ozs7OgyAACASVW5oFad+Pn5OboEAABgYhd96fOf//yngoKCVF5ebrd8wIABeuihhyRJq1atUtu2beXq6qqwsDAlJSXp9OnTtrb79u1Tly5d5OrqqhtvvFFr166VxWKxmx07dOiQBg8eLF9fX/n5+WnAgAHKycmR9Pvly0WLFmnVqlWyWCyyWCxav369JCkuLk433HCD3N3dFRYWpoSEBJWWllY4jtdee03BwcFyd3fX4MGDVVBQcM5jLi8vV3Jysho1aiQ3Nze1atVK77333sUOmTZs2KAOHTrIarUqMDBQTz31lN14/PnSJwAAwB9ddFD7y1/+ot9++03r1q2zLTt27Jg+++wzRUdHa+PGjRo2bJieeOIJ7d27V6+99poWLlyo6dOnS5LKyso0cOBAubu7a+vWrfrnP/+pp59+2m4fpaWlioqKkpeXlzZu3Kj09HR5enqqb9++Kikp0cSJEzV48GD17dtXeXl5ysvL0y233CJJ8vLy0sKFC7V3717NnTtXr7/+umbPnm3X//79+7VixQr9+9//1meffabt27fr8ccfP+cxJycna/HixXr11Ve1Z88ePfnkk/rrX/+qDRs2XHC8/vOf/+iOO+5QZGSkdu7cqVdeeUVvvvmmnn322Ysd8gqKi4tVWFho9wIAANXXRV/6rFOnjm6//XYtXbpUPXv2lCS99957qlu3rm677Tb16dNHTz31lGJiYiRJYWFhmjZtmiZNmqTExESlpqYqOztb69evV0BAgCRp+vTp6t27t20fy5cvV3l5ud544w1ZLBZJ0oIFC+Tr66v169erT58+cnNzU3Fxsa2PM5555hnbz6GhoZo4caKWLVumSZMm2ZafOnVKixcvVoMGDSRJL730kvr166eZM2dW6K+4uFjPPfec1q5dq06dOtmOadOmTXrttdfUrVu3847Xyy+/rODgYM2fP18Wi0XNmjXT4cOHFRcXp8mTJ8vJ6dKf40hOTlZSUtIlbwcAAKqmS7pHLTo6Wo888ohefvllWa1WLVmyRPfdd5+cnJy0c+dOpaen22bQpN9n0U6dOqWTJ08qKytLwcHBdoGoQ4cOdv3v3LlT+/fvl5eXl93yU6dOKTs7+7y1LV++XPPmzVN2draKiop0+vRpeXt727UJCQmxhTRJ6tSpk8rLy5WVlVUhqO3fv18nT560C5KSVFJSojZt2py3FknKzMxUp06dbIFTkjp37qyioiL99NNPCgkJuWAffxYfH6/x48fb3hcWFio4OPiS+wEAAFXDJQW1/v37yzAMrV69WpGRkdq4caPt8mJRUZGSkpI0aNCgCtu5urpeVP9FRUVq166dlixZUmFdvXr1zrndli1bFB0draSkJEVFRcnHx0fLli3TzJkzL/LIzl6LJK1evdou3EmS1Wq97H6vhNVqddi+AQDAtXdJQc3V1VWDBg3SkiVLtH//fjVt2lRt27aVJLVt21ZZWVlq0qTJWbdt2rSpDh06pCNHjsjf31+SlJGRYdembdu2Wr58uerXr19hNuwMFxcXlZWV2S3bvHmzGjZsaHfP248//lhh29zcXB0+fFhBQUGSpK+++kpOTk5q2rRphbY33nijrFarcnNzL3iZ82yaN2+u999/X4Zh2GbV0tPT5eXlpeuvv/6S+wMAADXPJd8oFR0drdWrV+utt95SdHS0bfnkyZO1ePFiJSUlac+ePcrMzNSyZcts94717t1bjRs3VkxMjHbt2qX09HTbujNBJjo6WnXr1tWAAQO0ceNGHTx4UOvXr9e4ceP0008/Sfr9/rNdu3YpKytLv/76q0pLSxUeHq7c3FwtW7ZM2dnZmjdvnj788MMKtbu6uiomJkY7d+7Uxo0bNW7cOA0ePLjCZU/p94cTJk6cqCeffFKLFi1Sdna2tm3bppdeekmLFi264Dg9/vjjOnTokMaOHat9+/Zp1apVSkxM1Pjx4y/r/jQAAFDzXHJi6NGjh/z8/JSVlaX777/ftjwqKkoff/yxPv/8c0VGRurmm2/W7Nmz1bBhQ0lSrVq1tHLlShUVFSkyMlIPP/ywbQbszKVRd3d3ffnllwoJCdGgQYPUvHlzjRgxQqdOnbLNsD3yyCNq2rSp2rdvr3r16ik9PV133XWXnnzySY0ZM0atW7fW5s2blZCQUKH2Jk2aaNCgQbrjjjvUp08fRURE6OWXXz7nsU6bNk0JCQlKTk5W8+bN1bdvX61evVqNGjW64Dg1aNBAn3zyib7++mu1atVKo0aN0ogRI+weegAAADgfi2EYhqN2np6eri5dumj//v1q3Lixo8qosgoLC+Xj46Pg2BVysro7uhwANVROSj9HlwBUKWc+vwsKCs55q9cZ1/QvE3z44Yfy9PRUeHi49u/fryeeeEKdO3cmpAEAAJzFNb1Z6vjx4xo9erSaNWum4cOHKzIyUqtWrbqWJVw1o0aNkqen51lfo0aNcnR5AACgGnDopc+q7OjRo+f8ywDe3t6qX79+pddwKVOnAADAHEx76bM6qV+//jUJYwAAoObieyIAAABMiqAGAABgUgQ1AAAAkyKoAQAAmBRBDQAAwKQIagAAACZFUAMAADApghoAAIBJEdQAAABMiqAGAABgUgQ1AAAAkyKoAQAAmBRBDQAAwKQIagAAACZFUAMAADApghoAAIBJEdQAAABMqrajC8CVa5G4Rk5Wd0eXAQCXJCeln6NLAEyPGTUAAACTIqgBAACYFEENAADApAhqAAAAJkVQMwmLxaKVK1c6ugwAAGAiBDUAAACTqhZBrby8XDNmzFCTJk1ktVoVEhKi6dOnS5K+++479ejRQ25ubrruuus0cuRIFRUV2W3/1ltv6aabbpLValVgYKDGjBljW5ebm6sBAwbI09NT3t7eGjx4sI4cOWJbP2XKFLVu3Vpvv/22QkND5ePjo/vuu0/Hjx+3tQkNDdWcOXPs9tm6dWtNmTLFtl6S7r77blksFtt7AABQs1WLoBYfH6+UlBQlJCRo7969Wrp0qfz9/XXixAlFRUWpTp06ysjI0Lvvvqu1a9faBbFXXnlFo0eP1siRI/Xdd9/po48+UpMmTST9HgAHDBigY8eOacOGDUpNTdWBAwc0ZMgQu/1nZ2dr5cqV+vjjj/Xxxx9rw4YNSklJuej6MzIyJEkLFixQXl6e7f2fFRcXq7Cw0O4FAACqryr/hbfHjx/X3LlzNX/+fMXExEiSGjdurC5duuj111/XqVOntHjxYnl4eEiS5s+fr/79++v555+Xv7+/nn32WU2YMEFPPPGErc/IyEhJUlpamr777jsdPHhQwcHBkqTFixfrpptuUkZGhq1deXm5Fi5cKC8vL0nSAw88oLS0NNus3oXUq1dPkuTr66uAgIBztktOTlZSUtKlDA8AAKjCqvyMWmZmpoqLi9WzZ8+zrmvVqpUtpElS586dVV5erqysLB09elSHDx8+67Zntg8ODraFNEm68cYb5evrq8zMTNuy0NBQW0iTpMDAQB09evRqHJ6d+Ph4FRQU2F6HDh266vsAAADmUeVn1Nzc3Byy7R85OzvbvbdYLCovL7e9d3JykmEYdm1KS0sveT9Wq1VWq/XyigQAAFVOlZ9RCw8Pl5ubm9LS0iqsa968uXbu3KkTJ07YlqWnp8vJyUlNmzaVl5eXQkNDz7rtme0PHTpkN3O1d+9e5efn68Ybb7zoGuvVq6e8vDzb+8LCQh08eNCujbOzs8rKyi66TwAAUP1V+aDm6uqquLg4TZo0SYsXL1Z2dra++uorvfnmm4qOjparq6tiYmK0e/durVu3TmPHjtUDDzwgf39/Sb8/tTlz5kzNmzdPP/zwg7Zt26aXXnpJktSrVy+1bNlS0dHR2rZtm77++msNGzZM3bp1U/v27S+6xh49eujtt9/Wxo0b9d133ykmJka1atWya3MmMP7888/673//e/UGCAAAVFlVPqhJUkJCgiZMmKDJkyerefPmGjJkiI4ePSp3d3etWbNGx44dU2RkpO6991717NlT8+fPt20bExOjOXPm6OWXX9ZNN92kO++8Uz/88IOk3y9hrlq1SnXq1FHXrl3Vq1cvhYWFafny5ZdUX3x8vLp166Y777xT/fr108CBA9W4cWO7NjNnzlRqaqqCg4PVpk2bKx8UAABQ5VmMP988hSqjsLBQPj4+Co5dISeru6PLAYBLkpPSz9ElAA5x5vO7oKBA3t7e521bLWbUAAAAqiOCGgAAgElV+a/ngLQ7KeqCU6cAAKDqYUYNAADApAhqAAAAJkVQAwAAMCmCGgAAgEkR1AAAAEyKoAYAAGBSBDUAAACTIqgBAACYFEENAADApAhqAAAAJkVQAwAAMCmCGgAAgEkR1AAAAEyKoAYAAGBSBDUAAACTIqgBAACYFEENAADApAhqAAAAJlXb0QXgyrVIXCMnq7ujywCAqyonpZ+jSwAcjhk1AAAAkyKoAQAAmBRBDQAAwKQIan8SGhqqOXPmOLoMAAAAHib4s4yMDHl4eDi6DAAAAILan9WrV8/RJUiSSkpK5OLi4ugyAACAA9W4S5/Hjx9XdHS0PDw8FBgYqNmzZ6t79+6KjY2VVPHSp8Vi0RtvvKG7775b7u7uCg8P10cffWTX50cffaTw8HC5urrqtttu06JFi2SxWJSfn29rs2nTJt16661yc3NTcHCwxo0bpxMnTtjWh4aGatq0aRo2bJi8vb01cuTIyhwGAABQBdS4oDZ+/Hilp6fro48+UmpqqjZu3Kht27add5ukpCQNHjxYu3bt0h133KHo6GgdO3ZMknTw4EHde++9GjhwoHbu3KlHH31UTz/9tN322dnZ6tu3r+655x7t2rVLy5cv16ZNmzRmzBi7di+++KJatWql7du3KyEhoUIdxcXFKiwstHsBAIDqq0YFtePHj2vRokV68cUX1bNnT7Vo0UILFixQWVnZebcbPny4hg4dqiZNmui5555TUVGRvv76a0nSa6+9pqZNm+qFF15Q06ZNdd9992n48OF22ycnJys6OlqxsbEKDw/XLbfconnz5mnx4sU6deqUrV2PHj00YcIENW7cWI0bN65QR3Jysnx8fGyv4ODgKx8UAABgWjUqqB04cEClpaXq0KGDbZmPj4+aNm163u0iIiJsP3t4eMjb21tHjx6VJGVlZSkyMtKu/R/7l6SdO3dq4cKF8vT0tL2ioqJUXl6ugwcP2tq1b9/+vHXEx8eroKDA9jp06ND5DxgAAFRpPExwEZydne3eWywWlZeXX/T2RUVFevTRRzVu3LgK60JCQmw/X+hpU6vVKqvVetH7BQAAVVuNCmphYWFydnZWRkaGLSAVFBTo+++/V9euXS+rz6ZNm+qTTz6xW5aRkWH3vm3bttq7d6+aNGlyeYUDAIAaqUZd+vTy8lJMTIz+9re/ad26ddqzZ49GjBghJycnWSyWy+rz0Ucf1b59+xQXF6fvv/9eK1as0MKFCyXJ1mdcXJw2b96sMWPGaMeOHfrhhx+0atWqCg8TAAAA/FGNCmqSNGvWLHXq1El33nmnevXqpc6dO6t58+ZydXW9rP4aNWqk9957Tx988IEiIiL0yiuv2J76PHOZMiIiQhs2bND333+vW2+9VW3atNHkyZMVFBR01Y4LAABUPxbDMAxHF+FIJ06cUIMGDTRz5kyNGDHiqvQ5ffp0vfrqq5V+s39hYeHvT3/GrpCT1b1S9wUA11pOSj9HlwBUijOf3wUFBfL29j5v2xp1j5okbd++Xfv27VOHDh1UUFCgqVOnSpIGDBhw2X2+/PLLioyM1HXXXaf09HS98MILXNYEAABXrMYFNen3L5bNysqSi4uL2rVrp40bN6pu3bqX3d8PP/ygZ599VseOHVNISIgmTJig+Pj4q1gxAACoiWr8pc+q7FKmTgEAgDlcyud3jXuYAAAAoKogqAEAAJgUQQ0AAMCkCGoAAAAmRVADAAAwKYIaAACASRHUAAAATIqgBgAAYFIENQAAAJMiqAEAAJgUQQ0AAMCkCGoAAAAmRVADAAAwKYIaAACASRHUAAAATIqgBgAAYFIENQAAAJMiqAEAAJhUbUcXgCvXInGNnKzuji4DAEwrJ6Wfo0sALgszagAAACZFUAMAADApghoAAIBJ1ZiglpOTI4vFoh07dkiS1q9fL4vFovz8fIfWBQAAcC41JqgFBwcrLy9PLVq0qNT9WCwWrVy5slL3AQAAaoYaE9Rq1aqlgIAA1a7t+AddS0pKHF0CAACoAqpdUCsvL9eMGTPUpEkTWa1WhYSEaPr06RUuff7Zb7/9pqFDh6pBgwZyd3dXy5Yt9c4779i16d69u8aNG6dJkybJz89PAQEBmjJlim19aGioJOnuu++WxWKxvZ8yZYpat26tN954Q40aNZKrq6skKTc3VwMGDJCnp6e8vb01ePBgHTly5GoPCQAAqKKqXVCLj49XSkqKEhIStHfvXi1dulT+/v4X3O7UqVNq166dVq9erd27d2vkyJF64IEH9PXXX9u1W7RokTw8PLR161bNmDFDU6dOVWpqqiQpIyNDkrRgwQLl5eXZ3kvS/v379f777+uDDz7Qjh07VF5ergEDBujYsWPasGGDUlNTdeDAAQ0ZMuScNRYXF6uwsNDuBQAAqi/HXwe8io4fP665c+dq/vz5iomJkSQ1btxYXbp0UU5Oznm3bdCggSZOnGh7P3bsWK1Zs0YrVqxQhw4dbMsjIiKUmJgoSQoPD9f8+fOVlpam3r17q169epIkX19fBQQE2PVfUlKixYsX29qkpqbqu+++08GDBxUcHCxJWrx4sW666SZlZGQoMjKyQo3JyclKSkq6xFEBAABVVbWaUcvMzFRxcbF69ux5yduWlZVp2rRpatmypfz8/OTp6ak1a9YoNzfXrl1ERITd+8DAQB09evSC/Tds2NAW0s7UGhwcbAtpknTjjTfK19dXmZmZZ+0jPj5eBQUFttehQ4cu5RABAEAVU61m1Nzc3C572xdeeEFz587VnDlz1LJlS3l4eCg2NrbCjf/Ozs527y0Wi8rLyy/Yv4eHx2XXdobVapXVar3ifgAAQNVQrWbUwsPD5ebmprS0tEveNj09XQMGDNBf//pXtWrVSmFhYfr+++8vuR9nZ2eVlZVdsF3z5s116NAhu1mxvXv3Kj8/XzfeeOMl7xcAAFQ/1Sqoubq6Ki4uTpMmTdLixYuVnZ2tr776Sm+++eYFtw0PD1dqaqo2b96szMxMPfroo5f1BGZoaKjS0tL0888/67///e852/Xq1UstW7ZUdHS0tm3bpq+//lrDhg1Tt27d1L59+0veLwAAqH6qVVCTpISEBE2YMEGTJ09W8+bNNWTIkIu6h+yZZ55R27ZtFRUVpe7duysgIEADBw685P3PnDlTqampCg4OVps2bc7ZzmKxaNWqVapTp466du2qXr16KSwsTMuXL7/kfQIAgOrJYhiG4egicHkKCwvl4+Oj4NgVcrK6O7ocADCtnJR+ji4BsDnz+V1QUCBvb+/ztq12M2oAAADVBUENAADApKrV13PUVLuToi44dQoAAKoeZtQAAABMiqAGAABgUgQ1AAAAkyKoAQAAmBRBDQAAwKQIagAAACZFUAMAADApghoAAIBJEdQAAABMiqAGAABgUgQ1AAAAkyKoAQAAmBRBDQAAwKQIagAAACZFUAMAADApghoAAIBJEdQAAABMiqAGAABgUrUdXQCuXIvENXKyuju6DADAFchJ6efoEmBCzKgBAACYFEENAADApAhqAAAAJkVQAwAAMCmCGgAAgEkR1AAAAEyqSga18vJyzZgxQ02aNJHValVISIimT58uSYqLi9MNN9wgd3d3hYWFKSEhQaWlpZKknJwcOTk56ZtvvrHrb86cOWrYsKHKy8slSbt379btt98uT09P+fv764EHHtCvv/5qa9+9e3eNGzdOkyZNkp+fnwICAjRlyhS7Pi0Wi9544w3dfffdcnd3V3h4uD766CO7NhfaDwAAqNmqZFCLj49XSkqKEhIStHfvXi1dulT+/v6SJC8vLy1cuFB79+7V3Llz9frrr2v27NmSpNDQUPXq1UsLFiyw62/BggUaPny4nJyclJ+frx49eqhNmzb65ptv9Nlnn+nIkSMaPHiw3TaLFi2Sh4eHtm7dqhkzZmjq1KlKTU21a5OUlKTBgwdr165duuOOOxQdHa1jx45J0kXv54+Ki4tVWFho9wIAANWXxTAMw9FFXIrjx4+rXr16mj9/vh5++OELtn/xxRe1bNky2yzaihUrNGrUKOXl5clqtWrbtm1q3769Dhw4oNDQUD377LPauHGj1qxZY+vjp59+UnBwsLKysnTDDTeoe/fuKisr08aNG21tOnTooB49eiglJUXS7zNqzzzzjKZNmyZJOnHihDw9PfXpp5+qb9++F7WfP5syZYqSkpIqLA+OXcEX3gJAFccX3tYchYWF8vHxUUFBgby9vc/btsrNqGVmZqq4uFg9e/Y86/rly5erc+fOCggIkKenp5555hnl5uba1g8cOFC1atXShx9+KElauHChbrvtNoWGhkqSdu7cqXXr1snT09P2atasmSQpOzvb1k9ERITdfgMDA3X06FG7ZX9s4+HhIW9vb1ubi93PH8XHx6ugoMD2OnTo0AXHCwAAVF1V7k9Iubm5nXPdli1bFB0draSkJEVFRcnHx0fLli3TzJkzbW1cXFw0bNgwLViwQIMGDdLSpUs1d+5c2/qioiL1799fzz//fIX+AwMDbT87OzvbrbNYLLZ73C6mzcXu54+sVqusVuu5Dh8AAFQzVS6ohYeHy83NTWlpaRUufW7evFkNGzbU008/bVv2448/Vujj4YcfVosWLfTyyy/r9OnTGjRokG1d27Zt9f777ys0NFS1a1fe8Fyr/QAAgKqryl36dHV1VVxcnCZNmqTFixcrOztbX331ld58802Fh4crNzdXy5YtU3Z2tubNm2e7xPlHzZs3180336y4uDgNHTrUbpZu9OjROnbsmIYOHaqMjAxlZ2drzZo1evDBB1VWVnbVjuNa7QcAAFRdVS6oSVJCQoImTJigyZMnq3nz5hoyZIiOHj2qu+66S08++aTGjBmj1q1ba/PmzUpISDhrHyNGjFBJSYkeeughu+VBQUFKT09XWVmZ+vTpo5YtWyo2Nla+vr5ycrp6w3Wt9gMAAKquKvfU59Uybdo0vfvuu9q1a5ejS7lsZ54a4alPAKj6eOqz5qjWT31eqaKiIu3evVvz58/X2LFjHV0OAADAOdW4oDZmzBi1a9dO3bt3r3DZEwAAwExq7KXP6uBSpk4BAIA5cOkTAACgGiCoAQAAmBRBDQAAwKQIagAAACZFUAMAADApghoAAIBJEdQAAABMiqAGAABgUgQ1AAAAkyKoAQAAmBRBDQAAwKQIagAAACZFUAMAADApghoAAIBJEdQAAABMiqAGAABgUgQ1AAAAkyKoAQAAmFRtRxeAK9cicY2crO6OLgMAUAPlpPRzdAnVGjNqAAAAJkVQAwAAMCmCGgAAgEkR1K6AYRgaOXKk/Pz8ZLFY5Ovrq9jY2Ivatnv37hfdFgAA1Ew8THAFPvvsMy1cuFDr169XWFiYnJyc5Obm5uiyAABANUFQuwLZ2dkKDAzULbfc4uhSAABANcSlz8s0fPhwjR07Vrm5ubJYLAoNDa1wOfPll19WeHi4XF1d5e/vr3vvvdeuj/Lyck2aNEl+fn4KCAjQlClTru1BAAAAUyOoXaa5c+dq6tSpuv7665WXl6eMjAy79d98843GjRunqVOnKisrS5999pm6du1q12bRokXy8PDQ1q1bNWPGDE2dOlWpqann3GdxcbEKCwvtXgAAoPri0udl8vHxkZeXl2rVqqWAgIAK63Nzc+Xh4aE777xTXl5eatiwodq0aWPXJiIiQomJiZKk8PBwzZ8/X2lpaerdu/dZ95mcnKykpKSrfzAAAMCUmFGrJL1791bDhg0VFhamBx54QEuWLNHJkyft2kRERNi9DwwM1NGjR8/ZZ3x8vAoKCmyvQ4cOVUrtAADAHAhqlcTLy0vbtm3TO++8o8DAQE2ePFmtWrVSfn6+rY2zs7PdNhaLReXl5efs02q1ytvb2+4FAACqL4JaJapdu7Z69eqlGTNmaNeuXcrJydEXX3zh6LIAAEAVwT1qleTjjz/WgQMH1LVrV9WpU0effPKJysvL1bRpU0eXBgAAqgiCWiXx9fXVBx98oClTpujUqVMKDw/XO++8o5tuusnRpQEAgCrCYhiG4egicHkKCwvl4+Oj4NgVcrK6O7ocAEANlJPSz9ElVDlnPr8LCgoueL8596gBAACYFEENAADApLhHrRrYnRTFV3UAAFANMaMGAABgUgQ1AAAAkyKoAQAAmBRBDQAAwKQIagAAACZFUAMAADApghoAAIBJEdQAAABMiqAGAABgUgQ1AAAAkyKoAQAAmBRBDQAAwKQIagAAACZFUAMAADApghoAAIBJEdQAAABMiqAGAABgUgQ1AAAAk6rt6AJw5VokrpGT1d3RZQAAUK3kpPRzdAnMqAEAAJgVQQ0AAMCkCGoAAAAmRVCrZFOmTFHr1q1t74cPH66BAwc6rB4AAFB18DDBNTZ37lwZhuHoMgAAQBVAUKskhmGorKyswnIfHx8HVAMAAKoiLn3+n+PHjys6OloeHh4KDAzU7Nmz1b17d8XGxkqS3n77bbVv315eXl4KCAjQ/fffr6NHj9q2X79+vSwWiz799FO1a9dOVqtVmzZtqrCfP1/6LC8v14wZM9SkSRNZrVaFhIRo+vTplX24AACgCiCo/Z/x48crPT1dH330kVJTU7Vx40Zt27bNtr60tFTTpk3Tzp07tXLlSuXk5Gj48OEV+nnqqaeUkpKizMxMRUREXHC/8fHxSklJUUJCgvbu3aulS5fK39//rG2Li4tVWFho9wIAANUXlz71+2zaokWLtHTpUvXs2VOStGDBAgUFBdnaPPTQQ7afw8LCNG/ePEVGRqqoqEienp62dVOnTlXv3r0ver9z587V/PnzFRMTI0lq3LixunTpctb2ycnJSkpKuuTjAwAAVRMzapIOHDig0tJSdejQwbbMx8dHTZs2tb3/9ttv1b9/f4WEhMjLy0vdunWTJOXm5tr11b59+4veb2ZmpoqLi23h8ELi4+NVUFBgex06dOii9wUAAKoeZtQuwokTJxQVFaWoqCgtWbJE9erVU25urqKiolRSUmLX1sPD46L7dXNzu6Q6rFarrFbrJW0DAACqLmbU9PulTGdnZ2VkZNiWFRQU6Pvvv5ck7du3T7/99ptSUlJ06623qlmzZnYPElyu8PBwubm5KS0t7Yr7AgAA1Q8zapK8vLwUExOjv/3tb/Lz81P9+vWVmJgoJycnWSwWhYSEyMXFRS+99JJGjRql3bt3a9q0aVe8X1dXV8XFxWnSpElycXFR586d9csvv2jPnj0aMWLEVTgyAABQlTGj9n9mzZqlTp066c4771SvXr3UuXNnNW/eXK6urqpXr54WLlyod999VzfeeKNSUlL04osvXpX9JiQkaMKECZo8ebKaN2+uIUOGXJXZOgAAUPVZDL4m/6xOnDihBg0aaObMmaad3SosLJSPj4+CY1fIyeru6HIAAKhWclL6VUq/Zz6/CwoK5O3tfd62XPr8P9u3b9e+ffvUoUMHFRQUaOrUqZKkAQMGOLgyAABQUxHU/uDFF19UVlaWXFxc1K5dO23cuFF169Z1dFkAAKCG4tJnFXYpU6cAAMAcLuXzm4cJAAAATIqgBgAAYFIENQAAAJMiqAEAAJgUQQ0AAMCkCGoAAAAmRVADAAAwKb7wtgo78xV4hYWFDq4EAABcrDOf2xfzVbYEtSrst99+kyQFBwc7uBIAAHCpjh8/Lh8fn/O2IahVYX5+fpKk3NzcC/5D1xSFhYUKDg7WoUOH+GsNYjz+jPGoiDGxx3jYYzzsXa3xMAxDx48fV1BQ0AXbEtSqMCen328x9PHx4RfoT7y9vRmTP2A87DEeFTEm9hgPe4yHvasxHhc7wcLDBAAAACZFUAMAADApgloVZrValZiYKKvV6uhSTIMxscd42GM8KmJM7DEe9hgPe44YD4txMc+GAgAA4JpjRg0AAMCkCGoAAAAmRVADAAAwKYIaAACASRHUqrB//OMfCg0Nlaurqzp27Kivv/7a0SVdE8nJyYqMjJSXl5fq16+vgQMHKisry65N9+7dZbFY7F6jRo1yUMWVa8qUKRWOtVmzZrb1p06d0ujRo3XdddfJ09NT99xzj44cOeLAiitfaGhohTGxWCwaPXq0pOp/fnz55Zfq37+/goKCZLFYtHLlSrv1hmFo8uTJCgwMlJubm3r16qUffvjBrs2xY8cUHR0tb29v+fr6asSIESoqKrqGR3H1nG88SktLFRcXp5YtW8rDw0NBQUEaNmyYDh8+bNfH2c6plJSUa3wkV8eFzo/hw4dXONa+ffvatalO54d04TE52/8nFotFL7zwgq1NZZ0jBLUqavny5Ro/frwSExO1bds2tWrVSlFRUTp69KijS6t0GzZs0OjRo/XVV18pNTVVpaWl6tOnj06cOGHX7pFHHlFeXp7tNWPGDAdVXPluuukmu2PdtGmTbd2TTz6pf//733r33Xe1YcMGHT58WIMGDXJgtZUvIyPDbjxSU1MlSX/5y19sbarz+XHixAm1atVK//jHP866fsaMGZo3b55effVVbd26VR4eHoqKitKpU6dsbaKjo7Vnzx6lpqbq448/1pdffqmRI0deq0O4qs43HidPntS2bduUkJCgbdu26YMPPlBWVpbuuuuuCm2nTp1qd86MHTv2WpR/1V3o/JCkvn372h3rO++8Y7e+Op0f0oXH5I9jkZeXp7feeksWi0X33HOPXbtKOUcMVEkdOnQwRo8ebXtfVlZmBAUFGcnJyQ6syjGOHj1qSDI2bNhgW9atWzfjiSeecFxR11BiYqLRqlWrs67Lz883nJ2djXfffde2LDMz05BkbNmy5RpV6HhPPPGE0bhxY6O8vNwwjJp1fkgyPvzwQ9v78vJyIyAgwHjhhRdsy/Lz8w2r1Wq88847hmEYxt69ew1JRkZGhq3Np59+algsFuM///nPNau9Mvx5PM7m66+/NiQZP/74o21Zw4YNjdmzZ1ducQ5wtvGIiYkxBgwYcM5tqvP5YRgXd44MGDDA6NGjh92yyjpHmFGrgkpKSvTtt9+qV69etmVOTk7q1auXtmzZ4sDKHKOgoEDS//8j9WcsWbJEdevWVYsWLRQfH6+TJ086orxr4ocfflBQUJDCwsIUHR2t3NxcSdK3336r0tJSu3OlWbNmCgkJqTHnSklJif71r3/poYceksVisS2vSefHHx08eFA///yz3Tnh4+Ojjh072s6JLVu2yNfXV+3bt7e16dWrl5ycnLR169ZrXvO1VlBQIIvFIl9fX7vlKSkpuu6669SmTRu98MILOn36tGMKvAbWr1+v+vXrq2nTpnrsscf022+/2dbV9PPjyJEjWr16tUaMGFFhXWWcI/xR9iro119/VVlZmfz9/e2W+/v7a9++fQ6qyjHKy8sVGxurzp07q0WLFrbl999/vxo2bKigoCDt2rVLcXFxysrK0gcffODAaitHx44dtXDhQjVt2lR5eXlKSkrSrbfeqt27d+vnn3+Wi4tLhQ8cf39//fzzz44p+BpbuXKl8vPzNXz4cNuymnR+/NmZf/ez/f9xZt3PP/+s+vXr262vXbu2/Pz8qv15c+rUKcXFxWno0KF2f3R73Lhxatu2rfz8/LR582bFx8crLy9Ps2bNcmC1laNv374aNGiQGjVqpOzsbP3973/X7bffri1btqhWrVo1+vyQpEWLFsnLy6vCLSSVdY4Q1FCljR49Wrt377a7J0uS3b0SLVu2VGBgoHr27Kns7Gw1btz4WpdZqW6//XbbzxEREerYsaMaNmyoFStWyM3NzYGVmcObb76p22+/XUFBQbZlNen8wMUrLS3V4MGDZRiGXnnlFbt148ePt/0cEREhFxcXPfroo0pOTq52f17pvvvus/3csmVLRUREqHHjxlq/fr169uzpwMrM4a233lJ0dLRcXV3tllfWOcKlzyqobt26qlWrVoUn944cOaKAgAAHVXXtjRkzRh9//LHWrVun66+//rxtO3bsKEnav3//tSjNoXx9fXXDDTdo//79CggIUElJifLz8+3a1JRz5ccff9TatWv18MMPn7ddTTo/zvy7n+//j4CAgAoPJp0+fVrHjh2rtufNmZD2448/KjU11W427Ww6duyo06dPKycn59oU6EBhYWGqW7eu7fejJp4fZ2zcuFFZWVkX/D9FunrnCEGtCnJxcVG7du2UlpZmW1ZeXq60tDR16tTJgZVdG4ZhaMyYMfrwww/1xRdfqFGjRhfcZseOHZKkwMDASq7O8YqKipSdna3AwEC1a9dOzs7OdudKVlaWcnNza8S5smDBAtWvX1/9+vU7b7uadH40atRIAQEBdudEYWGhtm7dajsnOnXqpPz8fH377be2Nl988YXKy8ttobY6ORPSfvjhB61du1bXXXfdBbfZsWOHnJycKlwCrI5++ukn/fbbb7bfj5p2fvzRm2++qXbt2qlVq1YXbHvVzpGr/ngCrolly5YZVqvVWLhwobF3715j5MiRhq+vr/Hzzz87urRK99hjjxk+Pj7G+vXrjby8PNvr5MmThmEYxv79+42pU6ca33zzjXHw4EFj1apVRlhYmNG1a1cHV145JkyYYKxfv944ePCgkZ6ebvTq1cuoW7eucfToUcMwDGPUqFFGSEiI8cUXXxjffPON0alTJ6NTp04OrrrylZWVGSEhIUZcXJzd8ppwfhw/ftzYvn27sX37dkOSMWvWLGP79u22pxhTUlIMX19fY9WqVcauXbuMAQMGGI0aNTL+97//2fro27ev0aZNG2Pr1q3Gpk2bjPDwcGPo0KGOOqQrcr7xKCkpMe666y7j+uuvN3bs2GH3f0pxcbFhGIaxefNmY/bs2caOHTuM7Oxs41//+pdRr149Y9iwYQ4+sstzvvE4fvy4MXHiRGPLli3GwYMHjbVr1xpt27Y1wsPDjVOnTtn6qE7nh2Fc+HfGMAyjoKDAcHd3N1555ZUK21fmOUJQq8JeeuklIyQkxHBxcTE6dOhgfPXVV44u6ZqQdNbXggULDMMwjNzcXKNr166Gn5+fYbVajSZNmhh/+9vfjIKCAscWXkmGDBliBAYGGi4uLkaDBg2MIUOGGPv377et/9///mc8/vjjRp06dQx3d3fj7rvvNvLy8hxY8bWxZs0aQ5KRlZVlt7wmnB/r1q076+9ITEyMYRi/f0VHQkKC4e/vb1itVqNnz54Vxum3334zhg4danh6ehre3t7Ggw8+aBw/ftwBR3PlzjceBw8ePOf/KevWrTMMwzC+/fZbo2PHjoaPj4/h6upqNG/e3HjuuefsgktVcr7xOHnypNGnTx+jXr16hrOzs9GwYUPjkUceqTAJUJ3OD8O48O+MYRjGa6+9Zri5uRn5+fkVtq/Mc8RiGIZxZXNyAAAAqAzcowYAAGBSBDUAAACTIqgBAACYFEENAADApAhqAAAAJkVQAwAAMCmCGgAAgEkR1AAAAEyKoAYAAGBSBDUAAACTIqgBAACYFEENAADApP4fcktX1RY4fokAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "thai_ingredient_df = create_ingredient_df(thai_df)\n", + "thai_ingredient_df.head(10).plot.barh()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "japanese_ingredient_df = create_ingredient_df(japanese_df)\n", + "japanese_ingredient_df.head(10).plot.barh()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "chinese_ingredient_df = create_ingredient_df(chinese_df)\n", + "chinese_ingredient_df.head(10).plot.barh()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "indian_ingredient_df = create_ingredient_df(indian_df)\n", + "indian_ingredient_df.head(10).plot.barh()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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5 rows × 380 columns

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" + ], + "text/plain": [ + " almond angelica anise anise_seed apple apple_brandy apricot \\\n", + "0 0 0 0 0 0 0 0 \n", + "1 1 0 0 0 0 0 0 \n", + "2 0 0 0 0 0 0 0 \n", + "3 0 0 0 0 0 0 0 \n", + "4 0 0 0 0 0 0 0 \n", + "\n", + " armagnac artemisia artichoke ... whiskey white_bread white_wine \\\n", + "0 0 0 0 ... 0 0 0 \n", + "1 0 0 0 ... 0 0 0 \n", + "2 0 0 0 ... 0 0 0 \n", + "3 0 0 0 ... 0 0 0 \n", + "4 0 0 0 ... 0 0 0 \n", + "\n", + " whole_grain_wheat_flour wine wood yam yeast yogurt zucchini \n", + "0 0 0 0 0 0 0 0 \n", + "1 0 0 0 0 0 0 0 \n", + "2 0 0 0 0 0 0 0 \n", + "3 0 0 0 0 0 0 0 \n", + "4 0 0 0 0 0 1 0 \n", + "\n", + "[5 rows x 380 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "feature_df = df.drop([\"cuisine\", \"Unnamed: 0\", \"rice\", \"garlic\", \"ginger\"], axis=1)\n", + "labels_df = df.cuisine\n", + "feature_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "oversample = SMOTE()\n", + "transformed_feature_df, transformed_label_df = oversample.fit_resample(feature_df, labels_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "new label count: cuisine\n", + "indian 799\n", + "thai 799\n", + "chinese 799\n", + "japanese 799\n", + "korean 799\n", + "Name: count, dtype: int64\n", + "old label count: cuisine\n", + "korean 799\n", + "indian 598\n", + "chinese 442\n", + "japanese 320\n", + "thai 289\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "print(f'new label count: {transformed_label_df.value_counts()}')\n", + "print(f'old label count: {df.cuisine.value_counts()}')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "transformed_df = pd.concat([transformed_label_df, transformed_feature_df], axis=1, join=\"outer\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 3995 entries, 0 to 3994\n", + "Columns: 381 entries, cuisine to zucchini\n", + "dtypes: int64(380), object(1)\n", + "memory usage: 11.6+ MB\n" + ] + } + ], + "source": [ + "transformed_df.head()\n", + "transformed_df.info()\n", + "transformed_df.to_csv(\"../data/cleaned_cuisines.csv\")" + ] + } + ], "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -10,19 +791,10 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": 3 + "version": "3.12.5" }, "orig_nbformat": 2 }, "nbformat": 4, - "nbformat_minor": 2, - "cells": [ - { - "source": [ - "# Delicious Asian and Indian Cuisines " - ], - "cell_type": "markdown", - "metadata": {} - } - ] -} \ No newline at end of file + "nbformat_minor": 2 +} diff --git a/4-Classification/data/cleaned_cuisines.csv b/4-Classification/data/cleaned_cuisines.csv index 84526cc22..71fe39f11 100644 --- a/4-Classification/data/cleaned_cuisines.csv +++ b/4-Classification/data/cleaned_cuisines.csv @@ -2447,1550 +2447,1550 @@ 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