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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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"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",
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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": [
"0.3236397181608923\n0.6061712937226016\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: 0.3315342327998989\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",
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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]"
],
"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>Month</th>\n <th>Variety</th>\n <th>City</th>\n <th>Package</th>\n <th>Price</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>70</th>\n <td>1</td>\n <td>3</td>\n <td>1</td>\n <td>0</td>\n <td>13.636364</td>\n </tr>\n <tr>\n <th>71</th>\n <td>1</td>\n <td>3</td>\n <td>1</td>\n <td>0</td>\n <td>16.363636</td>\n </tr>\n <tr>\n <th>72</th>\n <td>2</td>\n <td>3</td>\n <td>1</td>\n <td>0</td>\n <td>16.363636</td>\n </tr>\n <tr>\n <th>73</th>\n <td>2</td>\n <td>3</td>\n <td>1</td>\n <td>0</td>\n <td>15.454545</td>\n </tr>\n <tr>\n <th>74</th>\n <td>2</td>\n <td>3</td>\n <td>1</td>\n <td>0</td>\n <td>13.636364</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>1738</th>\n <td>1</td>\n <td>1</td>\n <td>9</td>\n <td>2</td>\n <td>30.000000</td>\n </tr>\n <tr>\n <th>1739</th>\n <td>1</td>\n <td>1</td>\n <td>9</td>\n <td>2</td>\n <td>28.750000</td>\n </tr>\n <tr>\n <th>1740</th>\n <td>1</td>\n <td>1</td>\n <td>9</td>\n <td>2</td>\n <td>25.750000</td>\n </tr>\n <tr>\n <th>1741</th>\n <td>1</td>\n <td>1</td>\n <td>9</td>\n <td>2</td>\n <td>24.000000</td>\n </tr>\n <tr>\n <th>1742</th>\n <td>1</td>\n <td>1</td>\n <td>9</td>\n <td>2</td>\n <td>24.000000</td>\n </tr>\n </tbody>\n</table>\n<p>415 rows × 5 columns</p>\n</div>"
},
"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 0x7fe5709abfd0>"
],
"text/html": "<style type=\"text/css\" >\n#T_374540ec_b1df_11eb_9c12_acde48001122row0_col0,#T_374540ec_b1df_11eb_9c12_acde48001122row1_col1,#T_374540ec_b1df_11eb_9c12_acde48001122row2_col2,#T_374540ec_b1df_11eb_9c12_acde48001122row3_col3,#T_374540ec_b1df_11eb_9c12_acde48001122row4_col4{\n background-color: #b40426;\n color: #f1f1f1;\n }#T_374540ec_b1df_11eb_9c12_acde48001122row0_col1{\n background-color: #ead4c8;\n color: #000000;\n }#T_374540ec_b1df_11eb_9c12_acde48001122row0_col2{\n background-color: #4961d2;\n color: #f1f1f1;\n }#T_374540ec_b1df_11eb_9c12_acde48001122row0_col3{\n background-color: #9bbcff;\n color: #000000;\n }#T_374540ec_b1df_11eb_9c12_acde48001122row0_col4{\n background-color: #bbd1f8;\n color: #000000;\n }#T_374540ec_b1df_11eb_9c12_acde48001122row1_col0{\n background-color: #9fbfff;\n color: #000000;\n }#T_374540ec_b1df_11eb_9c12_acde48001122row1_col2,#T_374540ec_b1df_11eb_9c12_acde48001122row1_col3,#T_374540ec_b1df_11eb_9c12_acde48001122row1_col4,#T_374540ec_b1df_11eb_9c12_acde48001122row2_col0,#T_374540ec_b1df_11eb_9c12_acde48001122row4_col1{\n background-color: #3b4cc0;\n color: #f1f1f1;\n }#T_374540ec_b1df_11eb_9c12_acde48001122row2_col1{\n background-color: #a9c6fd;\n color: #000000;\n }#T_374540ec_b1df_11eb_9c12_acde48001122row2_col3{\n background-color: #edd2c3;\n color: #000000;\n }#T_374540ec_b1df_11eb_9c12_acde48001122row2_col4{\n background-color: #f5c0a7;\n color: #000000;\n }#T_374540ec_b1df_11eb_9c12_acde48001122row3_col0{\n background-color: #455cce;\n color: #f1f1f1;\n }#T_374540ec_b1df_11eb_9c12_acde48001122row3_col1{\n background-color: #6485ec;\n color: #000000;\n }#T_374540ec_b1df_11eb_9c12_acde48001122row3_col2{\n background-color: #ccd9ed;\n color: #000000;\n }#T_374540ec_b1df_11eb_9c12_acde48001122row3_col4{\n background-color: #f08a6c;\n color: #000000;\n }#T_374540ec_b1df_11eb_9c12_acde48001122row4_col0{\n background-color: #445acc;\n color: #f1f1f1;\n }#T_374540ec_b1df_11eb_9c12_acde48001122row4_col2{\n background-color: #d2dbe8;\n color: #000000;\n }#T_374540ec_b1df_11eb_9c12_acde48001122row4_col3{\n background-color: #f39778;\n color: #000000;\n }</style><table id=\"T_374540ec_b1df_11eb_9c12_acde48001122\" ><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_374540ec_b1df_11eb_9c12_acde48001122level0_row0\" class=\"row_heading level0 row0\" >Month</th>\n <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row0_col0\" class=\"data row0 col0\" >1.000000</td>\n <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row0_col1\" class=\"data row0 col1\" >0.171330</td>\n <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row0_col2\" class=\"data row0 col2\" >-0.188728</td>\n <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row0_col3\" class=\"data row0 col3\" >-0.144847</td>\n <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row0_col4\" class=\"data row0 col4\" >-0.148783</td>\n </tr>\n <tr>\n <th id=\"T_374540ec_b1df_11eb_9c12_acde48001122level0_row1\" class=\"row_heading level0 row1\" >Variety</th>\n <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row1_col0\" class=\"data row1 col0\" >0.171330</td>\n <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row1_col1\" class=\"data row1 col1\" >1.000000</td>\n <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row1_col2\" class=\"data row1 col2\" >-0.248441</td>\n <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row1_col3\" class=\"data row1 col3\" >-0.614855</td>\n <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row1_col4\" class=\"data row1 col4\" >-0.863479</td>\n </tr>\n <tr>\n <th id=\"T_374540ec_b1df_11eb_9c12_acde48001122level0_row2\" class=\"row_heading level0 row2\" >City</th>\n <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row2_col0\" class=\"data row2 col0\" >-0.188728</td>\n <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row2_col1\" class=\"data row2 col1\" >-0.248441</td>\n <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row2_col2\" class=\"data row2 col2\" >1.000000</td>\n <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row2_col3\" class=\"data row2 col3\" >0.301604</td>\n <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row2_col4\" class=\"data row2 col4\" >0.323640</td>\n </tr>\n <tr>\n <th id=\"T_374540ec_b1df_11eb_9c12_acde48001122level0_row3\" class=\"row_heading level0 row3\" >Package</th>\n <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row3_col0\" class=\"data row3 col0\" >-0.144847</td>\n <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row3_col1\" class=\"data row3 col1\" >-0.614855</td>\n <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row3_col2\" class=\"data row3 col2\" >0.301604</td>\n <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row3_col3\" class=\"data row3 col3\" >1.000000</td>\n <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row3_col4\" class=\"data row3 col4\" >0.606171</td>\n </tr>\n <tr>\n <th id=\"T_374540ec_b1df_11eb_9c12_acde48001122level0_row4\" class=\"row_heading level0 row4\" >Price</th>\n <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row4_col0\" class=\"data row4 col0\" >-0.148783</td>\n <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row4_col1\" class=\"data row4 col1\" >-0.863479</td>\n <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row4_col2\" class=\"data row4 col2\" >0.323640</td>\n <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row4_col3\" class=\"data row4 col3\" >0.606171</td>\n <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row4_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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