diff --git a/2-Regression/1-Tools/README.md b/2-Regression/1-Tools/README.md index 6a276daed..066929b4f 100644 --- a/2-Regression/1-Tools/README.md +++ b/2-Regression/1-Tools/README.md @@ -152,6 +152,9 @@ plt.scatter(X_test, y_test, color='black') plt.plot(X_test, y_pred, color='blue', linewidth=3) plt.show() ``` + +![a scatterplot showing datapoints around diabetes](./images/scatterplot.png) + ✅ Think a bit about what's going on here. A straight line is running through many small dots of data, but what is it doing exactly? Can you see how you should be able to use this line to predict where a new, unseen data point should fit in relationship to the plot's y axis? Try to put into words the practical use of this model. Congratulations, you just built your first Linear Regression model, created a prediction with it, and displayed it in a plot! diff --git a/2-Regression/1-Tools/images/scatterplot.png b/2-Regression/1-Tools/images/scatterplot.png new file mode 100644 index 000000000..ba9f1610c Binary files /dev/null and b/2-Regression/1-Tools/images/scatterplot.png differ diff --git a/2-Regression/1-Tools/solution/notebook.ipynb b/2-Regression/1-Tools/solution/notebook.ipynb index 6653a2733..76a1634a6 100644 --- a/2-Regression/1-Tools/solution/notebook.ipynb +++ b/2-Regression/1-Tools/solution/notebook.ipynb @@ -24,7 +24,7 @@ "cells": [ { "source": [ - "## Linear Regression Solution" + "## Linear Regression for North American Pumpkins - Lesson 1" ], "cell_type": "markdown", "metadata": {} @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -98,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -114,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -125,7 +125,7 @@ ] }, "metadata": {}, - "execution_count": 6 + "execution_count": 5 } ], "source": [ @@ -142,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -158,15 +158,15 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
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\n" 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\n" }, "metadata": { "needs_background": "light" diff --git a/2-Regression/2-Data/README.md b/2-Regression/2-Data/README.md index df27787cf..0b2aacf37 100644 --- a/2-Regression/2-Data/README.md +++ b/2-Regression/2-Data/README.md @@ -16,7 +16,7 @@ The question you need answered will determine what type of ML algorithms you wil > infographic here -What if you are trying to correlate two points of data - like age to height? You can use a regression model, as shown in the previous lesson, to draw the classical straight line through the scatterplot of points to show how, with age, height tends to increase. Thus you can predict, for a given group of people, their height given their age. +What if you are trying to correlate two points of data - like age to height? You can use a linear regression model, as shown in the previous lesson, to draw the classical straight line through the scatterplot of points to show how, with age, height tends to increase. Thus you can predict, for a given group of people, their height given their age. > infographic here @@ -124,6 +124,8 @@ month = new_pumpkins.Month plt.scatter(price, month) plt.show() ``` +![A scatterplot showing price to month relationship](./images/scatterplot.png) + Is this a useful plot? Does anything about it surprise you? It's not particularly useful as all it does is display in your data as a spread of points in a given month. To get charts to display useful data, you usually need to group the data somehow. Let's try creating a plot where the y axis shows the months and the data demonstrates the distribution of data. @@ -135,6 +137,8 @@ new_pumpkins.groupby(['Month'])['Price'].mean().plot(kind='bar') plt.ylabel("Pumpkin Price") ``` +![A bar chart showing price to month relationship](./images/barchart.png) + This is a more useful data visualization! It seems to indicate that the highest price for pumpkins occurs in September and October. Does that meet your expectation? Why or why not? --- diff --git a/2-Regression/2-Data/images/barchart.png b/2-Regression/2-Data/images/barchart.png new file mode 100644 index 000000000..ce5a8d7ef Binary files /dev/null and b/2-Regression/2-Data/images/barchart.png differ diff --git a/2-Regression/2-Data/images/scatterplot.png b/2-Regression/2-Data/images/scatterplot.png new file mode 100644 index 000000000..ab8e30852 Binary files /dev/null and b/2-Regression/2-Data/images/scatterplot.png differ diff --git a/2-Regression/2-Data/solution/notebook.ipynb b/2-Regression/2-Data/solution/notebook.ipynb index ebbf072d7..4d8a3ea63 100644 --- a/2-Regression/2-Data/solution/notebook.ipynb +++ b/2-Regression/2-Data/solution/notebook.ipynb @@ -22,6 +22,13 @@ "nbformat": 4, "nbformat_minor": 2, "cells": [ + { + "source": [ + "## Linear Regression for Pumpkins - Lesson 2" + ], + "cell_type": "markdown", + "metadata": {} + }, { "cell_type": "code", "execution_count": 22, diff --git a/2-Regression/3-Linear/README.md b/2-Regression/3-Linear/README.md index ae1052537..4c7faafba 100644 --- a/2-Regression/3-Linear/README.md +++ b/2-Regression/3-Linear/README.md @@ -133,6 +133,8 @@ plt.ylabel('Price') plt.show() ``` +![A scatterplot showing package to price relationship](./images/linear.png) + And you can test the model against a hypothetical variety: ```python @@ -173,6 +175,8 @@ corr = poly_pumpkins.corr() corr.style.background_gradient(cmap='coolwarm') ``` +![A heatmap showing data correlation](./images/heatmap.png) + Looking at this chart, you can visualize the good correlation between Package and Price. So you should be able to create a somewhat better model than the last one. Build out the X and y columns: @@ -210,6 +214,9 @@ plt.ylabel('Price') plt.scatter(X,y, color="black") plt.show() ``` + +![A polynomial plot showing package to price relationship](./images/polynomial.png) + You can see a curved line that fits your data better. Let's check the model's accuracy: ```python diff --git a/2-Regression/3-Linear/images/heatmap.png b/2-Regression/3-Linear/images/heatmap.png new file mode 100644 index 000000000..202367f52 Binary files /dev/null and b/2-Regression/3-Linear/images/heatmap.png differ diff --git a/2-Regression/3-Linear/images/linear.png b/2-Regression/3-Linear/images/linear.png new file mode 100644 index 000000000..a9c8c5e85 Binary files /dev/null and b/2-Regression/3-Linear/images/linear.png differ diff --git a/2-Regression/3-Linear/images/polynomial.png b/2-Regression/3-Linear/images/polynomial.png new file mode 100644 index 000000000..1a36807c7 Binary files /dev/null and b/2-Regression/3-Linear/images/polynomial.png differ diff --git a/2-Regression/3-Linear/solution/notebook.ipynb b/2-Regression/3-Linear/solution/notebook.ipynb index 4e7b944e1..8d9809c3b 100644 --- a/2-Regression/3-Linear/solution/notebook.ipynb +++ b/2-Regression/3-Linear/solution/notebook.ipynb @@ -24,7 +24,7 @@ "cells": [ { "source": [ - "## Pumpkin Pricing\n", + "## Linear and Polynomial Regression for Pumpkin Pricing - Lesson 3\n", "\n", "Load up required libraries and dataset. Convert the data to a dataframe containing a subset of the data: \n", "\n", @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -71,7 +71,7 @@ "text/html": "
\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
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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5 rows × 26 columns

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" }, "metadata": {}, - "execution_count": 150 + "execution_count": 1 } ], "source": [ @@ -86,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -103,7 +103,7 @@ "text/html": "
\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
MonthVarietyCityPackageLow PriceHigh PricePrice
7013105313.636364
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" }, "metadata": {}, - "execution_count": 155 + "execution_count": 6 } ], "source": [ @@ -276,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": 156, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -286,7 +286,7 @@ }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -315,14 +315,14 @@ }, { "cell_type": "code", - "execution_count": 158, + "execution_count": 9, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
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}, "metadata": { @@ -490,7 +490,7 @@ }, { "cell_type": "code", - "execution_count": 164, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -509,7 +509,7 @@ }, { "cell_type": "code", - "execution_count": 165, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -520,7 +520,7 @@ ] }, "metadata": {}, - "execution_count": 165 + "execution_count": 16 } ], "source": [ diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md index 19f075950..3602022f3 100644 --- a/2-Regression/4-Logistic/README.md +++ b/2-Regression/4-Logistic/README.md @@ -1,4 +1,7 @@ -# Introduction to Machine Learning +# Logistic Regression to Predict Categories + +- orange or white by price +- Add a sketchnote if possible/appropriate @@ -42,7 +45,10 @@ code blocks ## [Topic 3] -🚀 Challenge: Add a challenge for students to work on collaboratively in class to enhance the project +--- +## 🚀Challenge + +Add a challenge for students to work on collaboratively in class to enhance the project Optional: add a screenshot of the completed lesson's UI if appropriate diff --git a/2-Regression/4-Logistic/notebook.ipynb b/2-Regression/4-Logistic/notebook.ipynb index e69de29bb..37b121694 100644 --- a/2-Regression/4-Logistic/notebook.ipynb +++ b/2-Regression/4-Logistic/notebook.ipynb @@ -0,0 +1,207 @@ +{ + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3-final" + }, + "orig_nbformat": 2, + "kernelspec": { + "name": "python3", + "display_name": "Python 3", + "language": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2, + "cells": [ + { + "source": [ + "## Pumpkin Varieties and Color\n", + "\n", + "Load up required libraries and dataset. Convert the data to a dataframe containing a subset of the data: \n", + "\n", + "Let's look at the relationship between color and variety" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "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]" + ], + "text/html": "
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0BALTIMORENaN24 inch binsNaNNaNNaN4/29/17270.0280.0270.0...NaNNaNNaNNaNNaNNaNENaNNaNNaN
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" + }, + "metadata": {}, + "execution_count": 44 + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n", + "\n", + "pumpkins.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\nInt64Index: 716 entries, 23 to 1752\nData columns (total 2 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 Color 716 non-null object\n 1 Variety 716 non-null object\ndtypes: object(2)\nmemory usage: 16.8+ KB\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Color 716\n", + "Variety 716\n", + "dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 45 + } + ], + "source": [ + "\n", + "new_columns = ['Variety', 'Color']\n", + "\n", + "\n", + "pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)\n", + "\n", + "\n", + "new_pumpkins = pd.DataFrame({'Color': pumpkins['Color'],'Variety': pumpkins['Variety']})\n", + "\n", + "new_pumpkins.dropna(inplace=True)\n", + "new_pumpkins.info()\n", + "\n", + "\n", + "\n", + "new_pumpkins.count()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 46 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
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\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 \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 \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 \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 \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 \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 \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 \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 \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 \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 \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 \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 \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": 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+ }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.bar('Color','Variety',data=new_pumpkins)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Color Variety\n", + "23 0 HOWDEN TYPE\n", + "24 0 HOWDEN TYPE\n", + "25 0 HOWDEN TYPE\n", + "26 0 HOWDEN TYPE\n", + "87 0 PIE TYPE\n", + "... ... ...\n", + "1748 0 MINIATURE\n", + "1749 0 MINIATURE\n", + "1750 2 MINIATURE\n", + "1751 0 MINIATURE\n", + "1752 2 MINIATURE\n", + "\n", + "[716 rows x 2 columns]" + ], + "text/html": "
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ColorVariety
230HOWDEN TYPE
240HOWDEN TYPE
250HOWDEN TYPE
260HOWDEN TYPE
870PIE TYPE
.........
17480MINIATURE
17490MINIATURE
17502MINIATURE
17510MINIATURE
17522MINIATURE
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716 rows × 2 columns

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" + }, + "metadata": {}, + "execution_count": 48 + } + ], + "source": [ + "from sklearn.preprocessing import LabelEncoder\n", + "\n", + "new_pumpkins.iloc[:, 0:-1] = new_pumpkins.iloc[:, 0:-1].apply(LabelEncoder().fit_transform)\n", + "\n", + "new_pumpkins\n", + "\n", + "#print(new_pumpkins['Color'].corr(new_pumpkins['Variety']))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ] +} \ No newline at end of file diff --git a/2-Regression/4-Logistic/solution/notebook.ipynb b/2-Regression/4-Logistic/solution/notebook.ipynb new file mode 100644 index 000000000..adcbbce3a --- /dev/null +++ b/2-Regression/4-Logistic/solution/notebook.ipynb @@ -0,0 +1,187 @@ +{ + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3-final" + }, + "orig_nbformat": 2, + "kernelspec": { + "name": "python3", + "display_name": "Python 3", + "language": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2, + "cells": [ + { + "source": [ + "## Pumpkin Pricing\n", + "\n", + "Load up required libraries and dataset. Convert the data to a dataframe containing a subset of the data: \n", + "\n", + "- Only get pumpkins priced by the bushel\n", + "- Convert the date to a month\n", + "- Calculate the price to be an average of high and low prices\n", + "- Convert the price to reflect the pricing by bushel quantity" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "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]" + ], + "text/html": "
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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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5 rows × 26 columns

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" + }, + "metadata": {}, + "execution_count": 2 + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n", + "\n", + "pumpkins.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Month Variety City Package Low Price High Price \\\n", + "70 9 PIE TYPE BALTIMORE 1 1/9 bushel cartons 15.0 15.0 \n", + "71 9 PIE TYPE BALTIMORE 1 1/9 bushel cartons 18.0 18.0 \n", + "72 10 PIE TYPE BALTIMORE 1 1/9 bushel cartons 18.0 18.0 \n", + "73 10 PIE TYPE BALTIMORE 1 1/9 bushel cartons 17.0 17.0 \n", + "74 10 PIE TYPE BALTIMORE 1 1/9 bushel cartons 15.0 15.0 \n", + "\n", + " Price \n", + "70 13.636364 \n", + "71 16.363636 \n", + "72 16.363636 \n", + "73 15.454545 \n", + "74 13.636364 " + ], + "text/html": "
\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
MonthVarietyCityPackageLow PriceHigh PricePrice
709PIE TYPEBALTIMORE1 1/9 bushel cartons15.015.013.636364
719PIE TYPEBALTIMORE1 1/9 bushel cartons18.018.016.363636
7210PIE TYPEBALTIMORE1 1/9 bushel cartons18.018.016.363636
7310PIE TYPEBALTIMORE1 1/9 bushel cartons17.017.015.454545
7410PIE TYPEBALTIMORE1 1/9 bushel cartons15.015.013.636364
\n
" + }, + "metadata": {}, + "execution_count": 3 + } + ], + "source": [ + "\n", + "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n", + "\n", + "new_columns = ['Package', 'Variety', 'City Name', 'Month', 'Low Price', 'High Price', 'Date', 'City Num', 'Variety Num']\n", + "\n", + "\n", + "pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)\n", + "\n", + "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n", + "\n", + "month = pd.DatetimeIndex(pumpkins['Date']).month\n", + "\n", + "\n", + "new_pumpkins = pd.DataFrame({'Month': month, 'Variety': pumpkins['Variety'], 'City': pumpkins['City Name'], 'Package': pumpkins['Package'], 'Low Price': pumpkins['Low Price'],'High Price': pumpkins['High Price'], 'Price': price})\n", + "\n", + "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/1.1\n", + "\n", + "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price*2\n", + "\n", + "new_pumpkins.head()\n" + ] + }, + { + "source": [ + "A basic scatterplot reminds us that we only have month data from August through December. We probably need more data to be able to draw conclusions in a linear fashion." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(array([ 7.5, 8. , 8.5, 9. , 9.5, 10. , 10.5, 11. , 11.5, 12. , 12.5]),\n", + " )" + ] + }, + "metadata": {}, + "execution_count": 4 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.scatter('Month','Price',data=new_pumpkins)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ] +} \ No newline at end of file