You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
ML-For-Beginners/2-Regression/4-Logistic/solution/notebook.ipynb

187 lines
85 KiB

{
"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": "<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>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 </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>BALTIMORE</td>\n <td>NaN</td>\n <td>24 inch bins</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>4/29/17</td>\n <td>270.0</td>\n <td>280.0</td>\n <td>270.0</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>E</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>1</th>\n <td>BALTIMORE</td>\n <td>NaN</td>\n <td>24 inch bins</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>5/6/17</td>\n <td>270.0</td>\n <td>280.0</td>\n <td>270.0</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>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 <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>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 <td>NaN</td>\n <td>N</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 26 columns</p>\n</div>"
},
"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": "<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>"
},
"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",
" <a list of 11 Text major ticklabel objects>)"
]
},
"metadata": {},
"execution_count": 4
},
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 432x288 with 1 Axes>",
"image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"261.427642pt\" version=\"1.1\" viewBox=\"0 0 368.925 261.427642\" width=\"368.925pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;}\n </style>\n </defs>\n <g id=\"figure_1\">\n <g id=\"patch_1\">\n <path d=\"M -0 261.427642 \nL 368.925 261.427642 \nL 368.925 0 \nL -0 0 \nz\n\" style=\"fill:none;\"/>\n </g>\n <g id=\"axes_1\">\n <g id=\"patch_2\">\n <path d=\"M 26.925 224.64 \nL 361.725 224.64 \nL 361.725 7.2 \nL 26.925 7.2 \nz\n\" style=\"fill:#ffffff;\"/>\n </g>\n <g id=\"PathCollection_1\">\n <defs>\n <path d=\"M 0 3 \nC 0.795609 3 1.55874 2.683901 2.12132 2.12132 \nC 2.683901 1.55874 3 0.795609 3 0 \nC 3 -0.795609 2.683901 -1.55874 2.12132 -2.12132 \nC 1.55874 -2.683901 0.795609 -3 0 -3 \nC -0.795609 -3 -1.55874 -2.683901 -2.12132 -2.12132 \nC -2.683901 -1.55874 -3 -0.795609 -3 0 \nC -3 0.795609 -2.683901 1.55874 -2.12132 2.12132 \nC -1.55874 2.683901 -0.795609 3 0 3 \nz\n\" id=\"m4e65fc1561\" style=\"stroke:#1f77b4;\"/>\n </defs>\n <g clip-path=\"url(#pf2c98c7c3f)\">\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"118.234091\" xlink:href=\"#m4e65fc1561\" y=\"201.474881\"/>\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"118.234091\" xlink:href=\"#m4e65fc1561\" y=\"188.193399\"/>\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m4e65fc1561\" y=\"188.193399\"/>\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m4e65fc1561\" y=\"192.62056\"/>\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m4e65fc1561\" y=\"201.474881\"/>\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m4e65fc1561\" y=\"188.193399\"/>\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m4e65fc1561\" y=\"192.62056\"/>\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m4e65fc1561\" y=\"189.300189\"/>\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m4e65fc1561\" y=\"201.474881\"/>\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m4e65fc1561\" y=\"192.62056\"/>\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m4e65fc1561\" y=\"189.300189\"/>\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m4e65fc1561\" y=\"201.474881\"/>\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m4e65fc1561\" y=\"192.62056\"/>\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m4e65fc1561\" y=\"189.300189\"/>\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m4e65fc1561\" y=\"201.474881\"/>\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m4e65fc1561\" y=\"194.83414\"/>\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m4e65fc1561\" y=\"192.62056\"/>\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"118.234091\" xlink:href=\"#m4e65fc1561\" y=\"188.193399\"/>\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"118.234091\" xlink:href=\"#m4e65fc1561\" y=\"197.047721\"/>\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"118.234091\" xlink:href=\"#m4e65fc1561\" y=\"188.193399\"/>\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"118.234091\" xlink:href=\"#m4e65fc1561\" y=\"197.047721\"/>\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"118.234091\" xlink:href=\"#m4e65fc1561\" y=\"197.047721\"/>\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"118.234091\" xlink:href=\"#m4e65fc1561\" y=\"197.047721\"/>\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"118.234091\" xlink:href=\"#m4e65fc1561\" y=\"92.566727\"/>\n <u
"image/png": "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
},
"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": []
}
]
}