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ML-For-Beginners/2-Regression/3-Linear/solution/notebook.ipynb

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"## 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",
"- 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"
],
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" 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]"
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"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"
]
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" Month Variety City Package Low Price High Price Price\n",
"70 1 3 1 0 5 3 13.636364\n",
"71 1 3 1 0 10 7 16.363636\n",
"72 2 3 1 0 10 7 16.363636\n",
"73 2 3 1 0 9 6 15.454545\n",
"74 2 3 1 0 5 3 13.636364"
],
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"source": [
"from sklearn.preprocessing import LabelEncoder\n",
"\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']\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",
"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.iloc[:, 0:-1] = new_pumpkins.iloc[:, 0:-1].apply(LabelEncoder().fit_transform)\n",
"\n",
"new_pumpkins.head()\n"
]
},
{
"source": [
"A 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."
],
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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"plt.scatter('Month','Price',data=new_pumpkins)"
]
},
{
"source": [
"Try some different correlations"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"0.32363971816089226\n0.6061712937226021\n"
]
}
],
"source": [
"print(new_pumpkins['City'].corr(new_pumpkins['Price']))\n",
"\n",
"print(new_pumpkins['Package'].corr(new_pumpkins['Price']))\n"
]
},
{
"source": [
"Drop unused columns"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\nInt64Index: 415 entries, 70 to 1742\nData columns (total 7 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 Month 415 non-null int64 \n 1 Variety 415 non-null int64 \n 2 City 415 non-null int64 \n 3 Package 415 non-null int64 \n 4 Low Price 415 non-null int64 \n 5 High Price 415 non-null int64 \n 6 Price 415 non-null float64\ndtypes: float64(1), int64(6)\nmemory usage: 25.9 KB\n"
]
}
],
"source": [
"\n",
"new_pumpkins.dropna(inplace=True)\n",
"new_pumpkins.info()\n",
"\n",
"\n"
]
},
{
"source": [
"Create a new dataframe"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Package Price\n",
"70 0 13.636364\n",
"71 0 16.363636\n",
"72 0 16.363636\n",
"73 0 15.454545\n",
"74 0 13.636364\n",
"... ... ...\n",
"1738 2 30.000000\n",
"1739 2 28.750000\n",
"1740 2 25.750000\n",
"1741 2 24.000000\n",
"1742 2 24.000000\n",
"\n",
"[415 rows x 2 columns]"
],
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Package</th>\n <th>Price</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>70</th>\n <td>0</td>\n <td>13.636364</td>\n </tr>\n <tr>\n <th>71</th>\n <td>0</td>\n <td>16.363636</td>\n </tr>\n <tr>\n <th>72</th>\n <td>0</td>\n <td>16.363636</td>\n </tr>\n <tr>\n <th>73</th>\n <td>0</td>\n <td>15.454545</td>\n </tr>\n <tr>\n <th>74</th>\n <td>0</td>\n <td>13.636364</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>1738</th>\n <td>2</td>\n <td>30.000000</td>\n </tr>\n <tr>\n <th>1739</th>\n <td>2</td>\n <td>28.750000</td>\n </tr>\n <tr>\n <th>1740</th>\n <td>2</td>\n <td>25.750000</td>\n </tr>\n <tr>\n <th>1741</th>\n <td>2</td>\n <td>24.000000</td>\n </tr>\n <tr>\n <th>1742</th>\n <td>2</td>\n <td>24.000000</td>\n </tr>\n </tbody>\n</table>\n<p>415 rows × 2 columns</p>\n</div>"
},
"metadata": {},
"execution_count": 6
}
],
"source": [
"new_columns = ['Package', 'Price']\n",
"lin_pumpkins = new_pumpkins.drop([c for c in new_pumpkins.columns if c not in new_columns], axis='columns')\n",
"\n",
"lin_pumpkins\n"
]
},
{
"source": [
"Set X and y arrays to correspond to Package and Price"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"X = lin_pumpkins.values[:, :1]\n",
"y = lin_pumpkins.values[:, 1:2]\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model Accuracy: 0.3315342327998987\n"
]
}
],
"source": [
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
"lin_reg = LinearRegression()\n",
"lin_reg.fit(X_train,y_train)\n",
"\n",
"pred = lin_reg.predict(X_test)\n",
"\n",
"accuracy_score = lin_reg.score(X_train,y_train)\n",
"print('Model Accuracy: ', accuracy_score)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"\n",
"plt.scatter(X_test, y_test, color='black')\n",
"plt.plot(X_test, pred, color='blue', linewidth=3)\n",
"\n",
"plt.xlabel('Package')\n",
"plt.ylabel('Price')\n",
"\n",
"plt.show()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[33.15655975]])"
]
},
"metadata": {},
"execution_count": 10
}
],
"source": [
"lin_reg.predict( np.array([ [2.75] ]) )"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Month Variety City Package Price\n",
"70 1 3 1 0 13.636364\n",
"71 1 3 1 0 16.363636\n",
"72 2 3 1 0 16.363636\n",
"73 2 3 1 0 15.454545\n",
"74 2 3 1 0 13.636364\n",
"... ... ... ... ... ...\n",
"1738 1 1 9 2 30.000000\n",
"1739 1 1 9 2 28.750000\n",
"1740 1 1 9 2 25.750000\n",
"1741 1 1 9 2 24.000000\n",
"1742 1 1 9 2 24.000000\n",
"\n",
"[415 rows x 5 columns]"
],
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},
"metadata": {},
"execution_count": 11
}
],
"source": [
"new_columns = ['Variety', 'Package', 'City', 'Month', 'Price']\n",
"poly_pumpkins = new_pumpkins.drop([c for c in new_pumpkins.columns if c not in new_columns], axis='columns')\n",
"\n",
"poly_pumpkins"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7f8320e9d580>"
],
"text/html": "<style type=\"text/css\" >\n #T_82ec828a_87fa_11eb_b754_f218988e26cdrow0_col0 {\n background-color: #b40426;\n color: #f1f1f1;\n } #T_82ec828a_87fa_11eb_b754_f218988e26cdrow0_col1 {\n background-color: #ead4c8;\n color: #000000;\n } #T_82ec828a_87fa_11eb_b754_f218988e26cdrow0_col2 {\n background-color: #4961d2;\n color: #f1f1f1;\n } #T_82ec828a_87fa_11eb_b754_f218988e26cdrow0_col3 {\n background-color: #9bbcff;\n color: #000000;\n } #T_82ec828a_87fa_11eb_b754_f218988e26cdrow0_col4 {\n background-color: #bbd1f8;\n color: #000000;\n } #T_82ec828a_87fa_11eb_b754_f218988e26cdrow1_col0 {\n background-color: #9fbfff;\n color: #000000;\n } #T_82ec828a_87fa_11eb_b754_f218988e26cdrow1_col1 {\n background-color: #b40426;\n color: #f1f1f1;\n } #T_82ec828a_87fa_11eb_b754_f218988e26cdrow1_col2 {\n background-color: #3b4cc0;\n color: #f1f1f1;\n } #T_82ec828a_87fa_11eb_b754_f218988e26cdrow1_col3 {\n background-color: #3b4cc0;\n color: #f1f1f1;\n } #T_82ec828a_87fa_11eb_b754_f218988e26cdrow1_col4 {\n background-color: #3b4cc0;\n color: #f1f1f1;\n } #T_82ec828a_87fa_11eb_b754_f218988e26cdrow2_col0 {\n background-color: #3b4cc0;\n color: #f1f1f1;\n } #T_82ec828a_87fa_11eb_b754_f218988e26cdrow2_col1 {\n background-color: #a9c6fd;\n color: #000000;\n } #T_82ec828a_87fa_11eb_b754_f218988e26cdrow2_col2 {\n background-color: #b40426;\n color: #f1f1f1;\n } #T_82ec828a_87fa_11eb_b754_f218988e26cdrow2_col3 {\n background-color: #edd2c3;\n color: #000000;\n } #T_82ec828a_87fa_11eb_b754_f218988e26cdrow2_col4 {\n background-color: #f5c0a7;\n color: #000000;\n } #T_82ec828a_87fa_11eb_b754_f218988e26cdrow3_col0 {\n background-color: #455cce;\n color: #f1f1f1;\n } #T_82ec828a_87fa_11eb_b754_f218988e26cdrow3_col1 {\n background-color: #6485ec;\n color: #000000;\n } #T_82ec828a_87fa_11eb_b754_f218988e26cdrow3_col2 {\n background-color: #ccd9ed;\n color: #000000;\n } #T_82ec828a_87fa_11eb_b754_f218988e26cdrow3_col3 {\n background-color: #b40426;\n color: #f1f1f1;\n } #T_82ec828a_87fa_11eb_b754_f218988e26cdrow3_col4 {\n background-color: #f08a6c;\n color: #000000;\n } #T_82ec828a_87fa_11eb_b754_f218988e26cdrow4_col0 {\n background-color: #445acc;\n color: #f1f1f1;\n } #T_82ec828a_87fa_11eb_b754_f218988e26cdrow4_col1 {\n background-color: #3b4cc0;\n color: #f1f1f1;\n } #T_82ec828a_87fa_11eb_b754_f218988e26cdrow4_col2 {\n background-color: #d2dbe8;\n color: #000000;\n } #T_82ec828a_87fa_11eb_b754_f218988e26cdrow4_col3 {\n background-color: #f39778;\n color: #000000;\n } #T_82ec828a_87fa_11eb_b754_f218988e26cdrow4_col4 {\n background-color: #b40426;\n color: #f1f1f1;\n }</style><table id=\"T_82ec828a_87fa_11eb_b754_f218988e26cd\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >Month</th> <th class=\"col_heading level0 col1\" >Variety</th> <th class=\"col_heading level0 col2\" >City</th> <th class=\"col_heading level0 col3\" >Package</th> <th class=\"col_heading level0 col4\" >Price</th> </tr></thead><tbody>\n <tr>\n <th id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdlevel0_row0\" class=\"row_heading level0 row0\" >Month</th>\n <td id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdrow0_col0\" class=\"data row0 col0\" >1.000000</td>\n <td id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdrow0_col1\" class=\"data row0 col1\" >0.171330</td>\n <td id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdrow0_col2\" class=\"data row0 col2\" >-0.188728</td>\n <td id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdrow0_col3\" class=\"data row0 col3\" >-0.144847</td>\n <td id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdrow0_col4\" class=\"data row0 col4\" >-0.148783</td>\n </tr>\n <tr>\n <th id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdlevel0_row1\" class=\"row_heading level0 row1\" >Variety</th>\n <td id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdrow1_col0\" class=\"data row1 col0\" >0.171330</td>\n <td id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdrow1_col1\" class=\"data row1 col1\" >1.000000</td>\n <td id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdrow1_col2\" class=\"data row1 col2\" >-0.248441</td>\n <td id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdrow1_col3\" class=\"data row1 col3\" >-0.614855</td>\n <td id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdrow1_col4\" class=\"data row1 col4\" >-0.863479</td>\n </tr>\n <tr>\n <th id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdlevel0_row2\" class=\"row_heading level0 row2\" >City</th>\n <td id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdrow2_col0\" class=\"data row2 col0\" >-0.188728</td>\n <td id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdrow2_col1\" class=\"data row2 col1\" >-0.248441</td>\n <td id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdrow2_col2\" class=\"data row2 col2\" >1.000000</td>\n <td id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdrow2_col3\" class=\"data row2 col3\" >0.301604</td>\n <td id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdrow2_col4\" class=\"data row2 col4\" >0.323640</td>\n </tr>\n <tr>\n <th id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdlevel0_row3\" class=\"row_heading level0 row3\" >Package</th>\n <td id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdrow3_col0\" class=\"data row3 col0\" >-0.144847</td>\n <td id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdrow3_col1\" class=\"data row3 col1\" >-0.614855</td>\n <td id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdrow3_col2\" class=\"data row3 col2\" >0.301604</td>\n <td id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdrow3_col3\" class=\"data row3 col3\" >1.000000</td>\n <td id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdrow3_col4\" class=\"data row3 col4\" >0.606171</td>\n </tr>\n <tr>\n <th id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdlevel0_row4\" class=\"row_heading level0 row4\" >Price</th>\n <td id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdrow4_col0\" class=\"data row4 col0\" >-0.148783</td>\n <td id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdrow4_col1\" class=\"data row4 col1\" >-0.863479</td>\n <td id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdrow4_col2\" class=\"data row4 col2\" >0.323640</td>\n <td id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdrow4_col3\" class=\"data row4 col3\" >0.606171</td>\n <td id=\"T_82ec828a_87fa_11eb_b754_f218988e26cdrow4_col4\" class=\"data row4 col4\" >1.000000</td>\n </tr>\n </tbody></table>"
},
"metadata": {},
"execution_count": 12
}
],
"source": [
"corr = poly_pumpkins.corr()\n",
"corr.style.background_gradient(cmap='coolwarm')"
]
},
{
"source": [
"Select the Package/Price columns"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"X=poly_pumpkins.iloc[:,3:4].values\n",
"y=poly_pumpkins.iloc[:,4:5].values\n"
]
},
{
"source": [
"Create Polynomial Regression model"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"from sklearn.preprocessing import PolynomialFeatures\n",
"from sklearn.pipeline import make_pipeline\n",
"\n",
"pipeline = make_pipeline(PolynomialFeatures(4), LinearRegression())\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
"\n",
"pipeline.fit(np.array(X_train), y_train)\n",
"\n",
"y_pred=pipeline.predict(X_test)\n",
"\n",
"df = pd.DataFrame({'x': X_test[:,0], 'y': y_pred[:,0]})\n",
"df.sort_values(by='x',inplace = True)\n",
"points = pd.DataFrame(df).to_numpy()\n",
"\n",
"plt.plot(points[:, 0], points[:, 1],color=\"blue\", linewidth=3)\n",
"plt.xlabel('Package')\n",
"plt.ylabel('Price')\n",
"\n",
"plt.scatter(X,y, color=\"black\")\n",
"\n",
"plt.show()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model Accuracy: 0.8537946517073784\n"
]
}
],
"source": [
"accuracy_score = pipeline.score(X_train,y_train)\n",
"print('Model Accuracy: ', accuracy_score)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[46.34509342]])"
]
},
"metadata": {},
"execution_count": 16
}
],
"source": [
"pipeline.predict( np.array([ [2.75] ]) )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
]
}
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