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

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lesson 3
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{
"cells": [
{
Make some changes to linear regression section to fix #543 (#562) * Rewrite linear regression section to include categorical features * Fix formatting
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"cell_type": "markdown",
"metadata": {},
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"source": [
polynomial regression notebook
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"## Pumpkin Pricing\n",
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"\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"
Make some changes to linear regression section to fix #543 (#562) * Rewrite linear regression section to include categorical features * Fix formatting
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]
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},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
Make some changes to linear regression section to fix #543 (#562) * Rewrite linear regression section to include categorical features * Fix formatting
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"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",
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" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>City Name</th>\n",
" <th>Type</th>\n",
" <th>Package</th>\n",
" <th>Variety</th>\n",
" <th>Sub Variety</th>\n",
" <th>Grade</th>\n",
" <th>Date</th>\n",
" <th>Low Price</th>\n",
" <th>High Price</th>\n",
" <th>Mostly Low</th>\n",
" <th>...</th>\n",
" <th>Unit of Sale</th>\n",
" <th>Quality</th>\n",
" <th>Condition</th>\n",
" <th>Appearance</th>\n",
" <th>Storage</th>\n",
" <th>Crop</th>\n",
" <th>Repack</th>\n",
" <th>Trans Mode</th>\n",
" <th>Unnamed: 24</th>\n",
" <th>Unnamed: 25</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>BALTIMORE</td>\n",
" <td>NaN</td>\n",
" <td>24 inch bins</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>5/6/17</td>\n",
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" <td>...</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>E</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>BALTIMORE</td>\n",
" <td>NaN</td>\n",
" <td>24 inch bins</td>\n",
" <td>HOWDEN TYPE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>9/24/16</td>\n",
" <td>160.0</td>\n",
" <td>160.0</td>\n",
" <td>160.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>N</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>BALTIMORE</td>\n",
" <td>NaN</td>\n",
" <td>24 inch bins</td>\n",
" <td>HOWDEN TYPE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>9/24/16</td>\n",
" <td>160.0</td>\n",
" <td>160.0</td>\n",
" <td>160.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>BALTIMORE</td>\n",
" <td>NaN</td>\n",
" <td>24 inch bins</td>\n",
" <td>HOWDEN TYPE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>11/5/16</td>\n",
" <td>90.0</td>\n",
" <td>100.0</td>\n",
" <td>90.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 26 columns</p>\n",
"</div>"
],
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"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]"
Make some changes to linear regression section to fix #543 (#562) * Rewrite linear regression section to include categorical features * Fix formatting
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]
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},
Make some changes to linear regression section to fix #543 (#562) * Rewrite linear regression section to include categorical features * Fix formatting
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
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"from datetime import datetime\n",
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"\n",
"pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
"\n",
"pumpkins.head()\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
Make some changes to linear regression section to fix #543 (#562) * Rewrite linear regression section to include categorical features * Fix formatting
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"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>Low Price</th>\n",
" <th>High Price</th>\n",
" <th>Price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>9</td>\n",
" <td>PIE TYPE</td>\n",
" <td>BALTIMORE</td>\n",
" <td>1 1/9 bushel cartons</td>\n",
" <td>15.0</td>\n",
" <td>15.0</td>\n",
" <td>13.636364</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>9</td>\n",
" <td>PIE TYPE</td>\n",
" <td>BALTIMORE</td>\n",
" <td>1 1/9 bushel cartons</td>\n",
" <td>18.0</td>\n",
" <td>18.0</td>\n",
" <td>16.363636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>10</td>\n",
" <td>PIE TYPE</td>\n",
" <td>BALTIMORE</td>\n",
" <td>1 1/9 bushel cartons</td>\n",
" <td>18.0</td>\n",
" <td>18.0</td>\n",
" <td>16.363636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>10</td>\n",
" <td>PIE TYPE</td>\n",
" <td>BALTIMORE</td>\n",
" <td>1 1/9 bushel cartons</td>\n",
" <td>17.0</td>\n",
" <td>17.0</td>\n",
" <td>15.454545</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>10</td>\n",
" <td>PIE TYPE</td>\n",
" <td>BALTIMORE</td>\n",
" <td>1 1/9 bushel cartons</td>\n",
" <td>15.0</td>\n",
" <td>15.0</td>\n",
" <td>13.636364</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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"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 "
Make some changes to linear regression section to fix #543 (#562) * Rewrite linear regression section to include categorical features * Fix formatting
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
],
"source": [
"pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
"\n",
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"new_columns = ['Package', 'Variety', 'City Name', 'Month', 'Low Price', 'High Price', 'Date']\n",
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"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",
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"day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n",
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"\n",
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"new_pumpkins = pd.DataFrame(\n",
" {'Month': month, \n",
" 'DayOfYear' : day_of_year, \n",
" 'Variety': pumpkins['Variety'], \n",
" 'City': pumpkins['City Name'], \n",
" 'Package': pumpkins['Package'], \n",
" 'Low Price': pumpkins['Low Price'],\n",
" 'High Price': pumpkins['High Price'], \n",
" 'Price': price})\n",
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"\n",
"new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/1.1\n",
"new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price*2\n",
"\n",
"new_pumpkins.head()\n"
]
},
{
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"cell_type": "markdown",
"metadata": {},
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"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."
Make some changes to linear regression section to fix #543 (#562) * Rewrite linear regression section to include categorical features * Fix formatting
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]
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},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
polynomial regression notebook
4 years ago
"(array([ 7.5, 8. , 8.5, 9. , 9.5, 10. , 10.5, 11. , 11.5, 12. , 12.5]),\n",
" <a list of 11 Text major ticklabel objects>)"
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]
},
Make some changes to linear regression section to fix #543 (#562) * Rewrite linear regression section to include categorical features * Fix formatting
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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},
{
"data": {
Make some changes to linear regression section to fix #543 (#562) * Rewrite linear regression section to include categorical features * Fix formatting
3 years ago
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polynomial regression notebook
4 years ago
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Make some changes to linear regression section to fix #543 (#562) * Rewrite linear regression section to include categorical features * Fix formatting
3 years ago
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
lesson 3
4 years ago
},
"metadata": {
"needs_background": "light"
Make some changes to linear regression section to fix #543 (#562) * Rewrite linear regression section to include categorical features * Fix formatting
3 years ago
},
"output_type": "display_data"
lesson 3
4 years ago
}
],
"source": [
"import matplotlib.pyplot as plt\n",
polynomial regression notebook
4 years ago
"plt.scatter('Month','Price',data=new_pumpkins)"
lesson 3
4 years ago
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
Make some changes to linear regression section to fix #543 (#562) * Rewrite linear regression section to include categorical features * Fix formatting
3 years ago
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