{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"df= pd.read_csv('car data.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"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>Car_Name</th>\n",
" <th>Year</th>\n",
" <th>Selling_Price</th>\n",
" <th>Present_Price</th>\n",
" <th>Kms_Driven</th>\n",
" <th>Fuel_Type</th>\n",
" <th>Seller_Type</th>\n",
" <th>Transmission</th>\n",
" <th>Owner</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ritz</td>\n",
" <td>2014</td>\n",
" <td>3.35</td>\n",
" <td>5.59</td>\n",
" <td>27000</td>\n",
" <td>Petrol</td>\n",
" <td>Dealer</td>\n",
" <td>Manual</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>sx4</td>\n",
" <td>2013</td>\n",
" <td>4.75</td>\n",
" <td>9.54</td>\n",
" <td>43000</td>\n",
" <td>Diesel</td>\n",
" <td>Dealer</td>\n",
" <td>Manual</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ciaz</td>\n",
" <td>2017</td>\n",
" <td>7.25</td>\n",
" <td>9.85</td>\n",
" <td>6900</td>\n",
" <td>Petrol</td>\n",
" <td>Dealer</td>\n",
" <td>Manual</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>wagon r</td>\n",
" <td>2011</td>\n",
" <td>2.85</td>\n",
" <td>4.15</td>\n",
" <td>5200</td>\n",
" <td>Petrol</td>\n",
" <td>Dealer</td>\n",
" <td>Manual</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>swift</td>\n",
" <td>2014</td>\n",
" <td>4.60</td>\n",
" <td>6.87</td>\n",
" <td>42450</td>\n",
" <td>Diesel</td>\n",
" <td>Dealer</td>\n",
" <td>Manual</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Car_Name Year Selling_Price Present_Price Kms_Driven Fuel_Type \\\n",
"0 ritz 2014 3.35 5.59 27000 Petrol \n",
"1 sx4 2013 4.75 9.54 43000 Diesel \n",
"2 ciaz 2017 7.25 9.85 6900 Petrol \n",
"3 wagon r 2011 2.85 4.15 5200 Petrol \n",
"4 swift 2014 4.60 6.87 42450 Diesel \n",
"\n",
" Seller_Type Transmission Owner \n",
"0 Dealer Manual 0 \n",
"1 Dealer Manual 0 \n",
"2 Dealer Manual 0 \n",
"3 Dealer Manual 0 \n",
"4 Dealer Manual 0 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(301, 9)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
" df.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Preprocessing"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Dealer' 'Individual']\n",
"['Manual' 'Automatic']\n",
"[0 1 3]\n",
"['Petrol' 'Diesel' 'CNG']\n"
]
}
],
"source": [
"print(df.Seller_Type.unique())\n",
"print(df.Transmission.unique())\n",
"print(df.Owner.unique())\n",
"print(df.Fuel_Type.unique())"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Car_Name 0\n",
"Year 0\n",
"Selling_Price 0\n",
"Present_Price 0\n",
"Kms_Driven 0\n",
"Fuel_Type 0\n",
"Seller_Type 0\n",
"Transmission 0\n",
"Owner 0\n",
"dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"##check null values\n",
"df.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" 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>Year</th>\n",
" <th>Selling_Price</th>\n",
" <th>Present_Price</th>\n",
" <th>Kms_Driven</th>\n",
" <th>Owner</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>301.000000</td>\n",
" <td>301.000000</td>\n",
" <td>301.000000</td>\n",
" <td>301.000000</td>\n",
" <td>301.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>2013.627907</td>\n",
" <td>4.661296</td>\n",
" <td>7.628472</td>\n",
" <td>36947.205980</td>\n",
" <td>0.043189</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>2.891554</td>\n",
" <td>5.082812</td>\n",
" <td>8.644115</td>\n",
" <td>38886.883882</td>\n",
" <td>0.247915</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>2003.000000</td>\n",
" <td>0.100000</td>\n",
" <td>0.320000</td>\n",
" <td>500.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2012.000000</td>\n",
" <td>0.900000</td>\n",
" <td>1.200000</td>\n",
" <td>15000.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>2014.000000</td>\n",
" <td>3.600000</td>\n",
" <td>6.400000</td>\n",
" <td>32000.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>2016.000000</td>\n",
" <td>6.000000</td>\n",
" <td>9.900000</td>\n",
" <td>48767.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>2018.000000</td>\n",
" <td>35.000000</td>\n",
" <td>92.600000</td>\n",
" <td>500000.000000</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Year Selling_Price Present_Price Kms_Driven Owner\n",
"count 301.000000 301.000000 301.000000 301.000000 301.000000\n",
"mean 2013.627907 4.661296 7.628472 36947.205980 0.043189\n",
"std 2.891554 5.082812 8.644115 38886.883882 0.247915\n",
"min 2003.000000 0.100000 0.320000 500.000000 0.000000\n",
"25% 2012.000000 0.900000 1.200000 15000.000000 0.000000\n",
"50% 2014.000000 3.600000 6.400000 32000.000000 0.000000\n",
"75% 2016.000000 6.000000 9.900000 48767.000000 0.000000\n",
"max 2018.000000 35.000000 92.600000 500000.000000 3.000000"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"In 'Year'-- year the car was made-- as no of years increase the price decrease\n",
"thus change 'year' and make a derived col-- no of year by calculating-->2021-'year' and add col no of years-> 6 yrs old.."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Car_Name', 'Year', 'Selling_Price', 'Present_Price', 'Kms_Driven',\n",
" 'Fuel_Type', 'Seller_Type', 'Transmission', 'Owner'],\n",
" dtype='object')"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"final_df=df[['Year', 'Selling_Price', 'Present_Price', 'Kms_Driven',\n",
" 'Fuel_Type', 'Seller_Type', 'Transmission', 'Owner']]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>Year</th>\n",
" <th>Selling_Price</th>\n",
" <th>Present_Price</th>\n",
" <th>Kms_Driven</th>\n",
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" <th>0</th>\n",
" <td>2014</td>\n",
" <td>3.35</td>\n",
" <td>5.59</td>\n",
" <td>27000</td>\n",
" <td>Petrol</td>\n",
" <td>Dealer</td>\n",
" <td>Manual</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2013</td>\n",
" <td>4.75</td>\n",
" <td>9.54</td>\n",
" <td>43000</td>\n",
" <td>Diesel</td>\n",
" <td>Dealer</td>\n",
" <td>Manual</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2017</td>\n",
" <td>7.25</td>\n",
" <td>9.85</td>\n",
" <td>6900</td>\n",
" <td>Petrol</td>\n",
" <td>Dealer</td>\n",
" <td>Manual</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2011</td>\n",
" <td>2.85</td>\n",
" <td>4.15</td>\n",
" <td>5200</td>\n",
" <td>Petrol</td>\n",
" <td>Dealer</td>\n",
" <td>Manual</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2014</td>\n",
" <td>4.60</td>\n",
" <td>6.87</td>\n",
" <td>42450</td>\n",
" <td>Diesel</td>\n",
" <td>Dealer</td>\n",
" <td>Manual</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Year Selling_Price Present_Price Kms_Driven Fuel_Type Seller_Type \\\n",
"0 2014 3.35 5.59 27000 Petrol Dealer \n",
"1 2013 4.75 9.54 43000 Diesel Dealer \n",
"2 2017 7.25 9.85 6900 Petrol Dealer \n",
"3 2011 2.85 4.15 5200 Petrol Dealer \n",
"4 2014 4.60 6.87 42450 Diesel Dealer \n",
"\n",
" Transmission Owner \n",
"0 Manual 0 \n",
"1 Manual 0 \n",
"2 Manual 0 \n",
"3 Manual 0 \n",
"4 Manual 0 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"final_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"final_df['Current_Year']=2021"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
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" <th>Kms_Driven</th>\n",
" <th>Fuel_Type</th>\n",
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" <td>2014</td>\n",
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" <td>Petrol</td>\n",
" <td>Dealer</td>\n",
" <td>Manual</td>\n",
" <td>0</td>\n",
" <td>2021</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2013</td>\n",
" <td>4.75</td>\n",
" <td>9.54</td>\n",
" <td>43000</td>\n",
" <td>Diesel</td>\n",
" <td>Dealer</td>\n",
" <td>Manual</td>\n",
" <td>0</td>\n",
" <td>2021</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2017</td>\n",
" <td>7.25</td>\n",
" <td>9.85</td>\n",
" <td>6900</td>\n",
" <td>Petrol</td>\n",
" <td>Dealer</td>\n",
" <td>Manual</td>\n",
" <td>0</td>\n",
" <td>2021</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2011</td>\n",
" <td>2.85</td>\n",
" <td>4.15</td>\n",
" <td>5200</td>\n",
" <td>Petrol</td>\n",
" <td>Dealer</td>\n",
" <td>Manual</td>\n",
" <td>0</td>\n",
" <td>2021</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2014</td>\n",
" <td>4.60</td>\n",
" <td>6.87</td>\n",
" <td>42450</td>\n",
" <td>Diesel</td>\n",
" <td>Dealer</td>\n",
" <td>Manual</td>\n",
" <td>0</td>\n",
" <td>2021</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Year Selling_Price Present_Price Kms_Driven Fuel_Type Seller_Type \\\n",
"0 2014 3.35 5.59 27000 Petrol Dealer \n",
"1 2013 4.75 9.54 43000 Diesel Dealer \n",
"2 2017 7.25 9.85 6900 Petrol Dealer \n",
"3 2011 2.85 4.15 5200 Petrol Dealer \n",
"4 2014 4.60 6.87 42450 Diesel Dealer \n",
"\n",
" Transmission Owner Current_Year \n",
"0 Manual 0 2021 \n",
"1 Manual 0 2021 \n",
"2 Manual 0 2021 \n",
"3 Manual 0 2021 \n",
"4 Manual 0 2021 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"final_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Year</th>\n",
" <th>Selling_Price</th>\n",
" <th>Present_Price</th>\n",
" <th>Kms_Driven</th>\n",
" <th>Fuel_Type</th>\n",
" <th>Seller_Type</th>\n",
" <th>Transmission</th>\n",
" <th>Owner</th>\n",
" <th>Current_Year</th>\n",
" <th>No_year</th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2014</td>\n",
" <td>3.35</td>\n",
" <td>5.59</td>\n",
" <td>27000</td>\n",
" <td>Petrol</td>\n",
" <td>Dealer</td>\n",
" <td>Manual</td>\n",
" <td>0</td>\n",
" <td>2021</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2013</td>\n",
" <td>4.75</td>\n",
" <td>9.54</td>\n",
" <td>43000</td>\n",
" <td>Diesel</td>\n",
" <td>Dealer</td>\n",
" <td>Manual</td>\n",
" <td>0</td>\n",
" <td>2021</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2017</td>\n",
" <td>7.25</td>\n",
" <td>9.85</td>\n",
" <td>6900</td>\n",
" <td>Petrol</td>\n",
" <td>Dealer</td>\n",
" <td>Manual</td>\n",
" <td>0</td>\n",
" <td>2021</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2011</td>\n",
" <td>2.85</td>\n",
" <td>4.15</td>\n",
" <td>5200</td>\n",
" <td>Petrol</td>\n",
" <td>Dealer</td>\n",
" <td>Manual</td>\n",
" <td>0</td>\n",
" <td>2021</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2014</td>\n",
" <td>4.60</td>\n",
" <td>6.87</td>\n",
" <td>42450</td>\n",
" <td>Diesel</td>\n",
" <td>Dealer</td>\n",
" <td>Manual</td>\n",
" <td>0</td>\n",
" <td>2021</td>\n",
" <td>7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Year Selling_Price Present_Price Kms_Driven Fuel_Type Seller_Type \\\n",
"0 2014 3.35 5.59 27000 Petrol Dealer \n",
"1 2013 4.75 9.54 43000 Diesel Dealer \n",
"2 2017 7.25 9.85 6900 Petrol Dealer \n",
"3 2011 2.85 4.15 5200 Petrol Dealer \n",
"4 2014 4.60 6.87 42450 Diesel Dealer \n",
"\n",
" Transmission Owner Current_Year No_year \n",
"0 Manual 0 2021 7 \n",
"1 Manual 0 2021 8 \n",
"2 Manual 0 2021 4 \n",
"3 Manual 0 2021 10 \n",
"4 Manual 0 2021 7 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"final_df['No_year']= final_df['Current_Year']-final_df['Year']\n",
"final_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"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>Selling_Price</th>\n",
" <th>Present_Price</th>\n",
" <th>Kms_Driven</th>\n",
" <th>Fuel_Type</th>\n",
" <th>Seller_Type</th>\n",
" <th>Transmission</th>\n",
" <th>Owner</th>\n",
" <th>No_year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3.35</td>\n",
" <td>5.59</td>\n",
" <td>27000</td>\n",
" <td>Petrol</td>\n",
" <td>Dealer</td>\n",
" <td>Manual</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.75</td>\n",
" <td>9.54</td>\n",
" <td>43000</td>\n",
" <td>Diesel</td>\n",
" <td>Dealer</td>\n",
" <td>Manual</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7.25</td>\n",
" <td>9.85</td>\n",
" <td>6900</td>\n",
" <td>Petrol</td>\n",
" <td>Dealer</td>\n",
" <td>Manual</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2.85</td>\n",
" <td>4.15</td>\n",
" <td>5200</td>\n",
" <td>Petrol</td>\n",
" <td>Dealer</td>\n",
" <td>Manual</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4.60</td>\n",
" <td>6.87</td>\n",
" <td>42450</td>\n",
" <td>Diesel</td>\n",
" <td>Dealer</td>\n",
" <td>Manual</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Selling_Price Present_Price Kms_Driven Fuel_Type Seller_Type \\\n",
"0 3.35 5.59 27000 Petrol Dealer \n",
"1 4.75 9.54 43000 Diesel Dealer \n",
"2 7.25 9.85 6900 Petrol Dealer \n",
"3 2.85 4.15 5200 Petrol Dealer \n",
"4 4.60 6.87 42450 Diesel Dealer \n",
"\n",
" Transmission Owner No_year \n",
"0 Manual 0 7 \n",
"1 Manual 0 8 \n",
"2 Manual 0 4 \n",
"3 Manual 0 10 \n",
"4 Manual 0 7 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#drop irrelevent data\n",
"final_df.drop(columns=['Year', 'Current_Year'], inplace=True, axis=1)\n",
"final_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 301 entries, 0 to 300\n",
"Data columns (total 8 columns):\n",
"Selling_Price 301 non-null float64\n",
"Present_Price 301 non-null float64\n",
"Kms_Driven 301 non-null int64\n",
"Fuel_Type 301 non-null object\n",
"Seller_Type 301 non-null object\n",
"Transmission 301 non-null object\n",
"Owner 301 non-null int64\n",
"No_year 301 non-null int64\n",
"dtypes: float64(2), int64(3), object(3)\n",
"memory usage: 18.9+ KB\n"
]
}
],
"source": [
"final_df.info()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import OneHotEncoder"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"#Since the no of categories very less --> use one hot encoding\n",
"#encoder= OneHotEncoder(handle_unknown='ignore')\n",
"#encoder.fit_transform(final_df)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"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>Selling_Price</th>\n",
" <th>Present_Price</th>\n",
" <th>Kms_Driven</th>\n",
" <th>Owner</th>\n",
" <th>No_year</th>\n",
" <th>Fuel_Type_Diesel</th>\n",
" <th>Fuel_Type_Petrol</th>\n",
" <th>Seller_Type_Individual</th>\n",
" <th>Transmission_Manual</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3.35</td>\n",
" <td>5.59</td>\n",
" <td>27000</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.75</td>\n",
" <td>9.54</td>\n",
" <td>43000</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7.25</td>\n",
" <td>9.85</td>\n",
" <td>6900</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2.85</td>\n",
" <td>4.15</td>\n",
" <td>5200</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4.60</td>\n",
" <td>6.87</td>\n",
" <td>42450</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Selling_Price Present_Price Kms_Driven Owner No_year Fuel_Type_Diesel \\\n",
"0 3.35 5.59 27000 0 7 0 \n",
"1 4.75 9.54 43000 0 8 1 \n",
"2 7.25 9.85 6900 0 4 0 \n",
"3 2.85 4.15 5200 0 10 0 \n",
"4 4.60 6.87 42450 0 7 1 \n",
"\n",
" Fuel_Type_Petrol Seller_Type_Individual Transmission_Manual \n",
"0 1 0 1 \n",
"1 0 0 1 \n",
"2 1 0 1 \n",
"3 1 0 1 \n",
"4 0 0 1 "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"final_df=pd.get_dummies(final_df, drop_first=True)\n",
"final_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"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>Selling_Price</th>\n",
" <th>Present_Price</th>\n",
" <th>Kms_Driven</th>\n",
" <th>Owner</th>\n",
" <th>No_year</th>\n",
" <th>Fuel_Type_Diesel</th>\n",
" <th>Fuel_Type_Petrol</th>\n",
" <th>Seller_Type_Individual</th>\n",
" <th>Transmission_Manual</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Selling_Price</th>\n",
" <td>1.000000</td>\n",
" <td>0.878983</td>\n",
" <td>0.029187</td>\n",
" <td>-0.088344</td>\n",
" <td>-0.236141</td>\n",
" <td>0.552339</td>\n",
" <td>-0.540571</td>\n",
" <td>-0.550724</td>\n",
" <td>-0.367128</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Present_Price</th>\n",
" <td>0.878983</td>\n",
" <td>1.000000</td>\n",
" <td>0.203647</td>\n",
" <td>0.008057</td>\n",
" <td>0.047584</td>\n",
" <td>0.473306</td>\n",
" <td>-0.465244</td>\n",
" <td>-0.512030</td>\n",
" <td>-0.348715</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kms_Driven</th>\n",
" <td>0.029187</td>\n",
" <td>0.203647</td>\n",
" <td>1.000000</td>\n",
" <td>0.089216</td>\n",
" <td>0.524342</td>\n",
" <td>0.172515</td>\n",
" <td>-0.172874</td>\n",
" <td>-0.101419</td>\n",
" <td>-0.162510</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Owner</th>\n",
" <td>-0.088344</td>\n",
" <td>0.008057</td>\n",
" <td>0.089216</td>\n",
" <td>1.000000</td>\n",
" <td>0.182104</td>\n",
" <td>-0.053469</td>\n",
" <td>0.055687</td>\n",
" <td>0.124269</td>\n",
" <td>-0.050316</td>\n",
" </tr>\n",
" <tr>\n",
" <th>No_year</th>\n",
" <td>-0.236141</td>\n",
" <td>0.047584</td>\n",
" <td>0.524342</td>\n",
" <td>0.182104</td>\n",
" <td>1.000000</td>\n",
" <td>-0.064315</td>\n",
" <td>0.059959</td>\n",
" <td>0.039896</td>\n",
" <td>-0.000394</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fuel_Type_Diesel</th>\n",
" <td>0.552339</td>\n",
" <td>0.473306</td>\n",
" <td>0.172515</td>\n",
" <td>-0.053469</td>\n",
" <td>-0.064315</td>\n",
" <td>1.000000</td>\n",
" <td>-0.979648</td>\n",
" <td>-0.350467</td>\n",
" <td>-0.098643</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fuel_Type_Petrol</th>\n",
" <td>-0.540571</td>\n",
" <td>-0.465244</td>\n",
" <td>-0.172874</td>\n",
" <td>0.055687</td>\n",
" <td>0.059959</td>\n",
" <td>-0.979648</td>\n",
" <td>1.000000</td>\n",
" <td>0.358321</td>\n",
" <td>0.091013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Seller_Type_Individual</th>\n",
" <td>-0.550724</td>\n",
" <td>-0.512030</td>\n",
" <td>-0.101419</td>\n",
" <td>0.124269</td>\n",
" <td>0.039896</td>\n",
" <td>-0.350467</td>\n",
" <td>0.358321</td>\n",
" <td>1.000000</td>\n",
" <td>0.063240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Transmission_Manual</th>\n",
" <td>-0.367128</td>\n",
" <td>-0.348715</td>\n",
" <td>-0.162510</td>\n",
" <td>-0.050316</td>\n",
" <td>-0.000394</td>\n",
" <td>-0.098643</td>\n",
" <td>0.091013</td>\n",
" <td>0.063240</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Selling_Price Present_Price Kms_Driven Owner \\\n",
"Selling_Price 1.000000 0.878983 0.029187 -0.088344 \n",
"Present_Price 0.878983 1.000000 0.203647 0.008057 \n",
"Kms_Driven 0.029187 0.203647 1.000000 0.089216 \n",
"Owner -0.088344 0.008057 0.089216 1.000000 \n",
"No_year -0.236141 0.047584 0.524342 0.182104 \n",
"Fuel_Type_Diesel 0.552339 0.473306 0.172515 -0.053469 \n",
"Fuel_Type_Petrol -0.540571 -0.465244 -0.172874 0.055687 \n",
"Seller_Type_Individual -0.550724 -0.512030 -0.101419 0.124269 \n",
"Transmission_Manual -0.367128 -0.348715 -0.162510 -0.050316 \n",
"\n",
" No_year Fuel_Type_Diesel Fuel_Type_Petrol \\\n",
"Selling_Price -0.236141 0.552339 -0.540571 \n",
"Present_Price 0.047584 0.473306 -0.465244 \n",
"Kms_Driven 0.524342 0.172515 -0.172874 \n",
"Owner 0.182104 -0.053469 0.055687 \n",
"No_year 1.000000 -0.064315 0.059959 \n",
"Fuel_Type_Diesel -0.064315 1.000000 -0.979648 \n",
"Fuel_Type_Petrol 0.059959 -0.979648 1.000000 \n",
"Seller_Type_Individual 0.039896 -0.350467 0.358321 \n",
"Transmission_Manual -0.000394 -0.098643 0.091013 \n",
"\n",
" Seller_Type_Individual Transmission_Manual \n",
"Selling_Price -0.550724 -0.367128 \n",
"Present_Price -0.512030 -0.348715 \n",
"Kms_Driven -0.101419 -0.162510 \n",
"Owner 0.124269 -0.050316 \n",
"No_year 0.039896 -0.000394 \n",
"Fuel_Type_Diesel -0.350467 -0.098643 \n",
"Fuel_Type_Petrol 0.358321 0.091013 \n",
"Seller_Type_Individual 1.000000 0.063240 \n",
"Transmission_Manual 0.063240 1.000000 "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"final_df.corr()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.PairGrid at 0x2c7cf3477f0>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1620x1620 with 90 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.pairplot(final_df)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x1440 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#plot heatmap\n",
"corrmat=final_df.corr() \n",
"top_corr_features=corrmat.index\n",
"plt.figure(figsize=(20,20))\n",
"g=sns.heatmap(final_df[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train -test split"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"#independent and dependent features\n",
"x=final_df.iloc[:, 1:]\n",
"y=final_df.iloc[:, 0] "
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 3.35\n",
"1 4.75\n",
"2 7.25\n",
"3 2.85\n",
"4 4.60\n",
"Name: Selling_Price, dtype: float64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y.head()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ExtraTreesRegressor()"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"###Feature Importance-- since its a Regressor model\n",
"from sklearn.ensemble import ExtraTreesRegressor\n",
"model= ExtraTreesRegressor()\n",
"model.fit(x,y)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.38884305 0.04472866 0.00042956 0.07515236 0.22917119 0.00766662\n",
" 0.11291905 0.14108951]\n"
]
}
],
"source": [
"print(model.feature_importances_)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#plot graph of feature importance for better visualization\n",
"feat_imp= pd.Series(model.feature_importances_, index=x.columns)\n",
"feat_imp.nlargest(5).plot(kind='barh')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"x_train, x_test, y_train, y_test= train_test_split(x, y, test_size=0.2)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(240, 8)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_train.shape"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestRegressor\n",
"rf_random= RandomForestRegressor()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200]\n"
]
}
],
"source": [
"#HyperParameters\n",
"n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
"print(n_estimators)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import RandomizedSearchCV"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"#Randomized Search CV\n",
"\n",
"# Number of trees in random forest\n",
"n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
"# Number of features to consider at every split\n",
"max_features = ['auto', 'sqrt']\n",
"# Maximum number of levels in tree\n",
"max_depth = [int(x) for x in np.linspace(5, 30, num = 6)]\n",
"# max_depth.append(None)\n",
"# Minimum number of samples required to split a node\n",
"min_samples_split = [2, 5, 10, 15, 100]\n",
"# Minimum number of samples required at each leaf node\n",
"min_samples_leaf = [1, 2, 5, 10]"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'n_estimators': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200], 'max_features': ['auto', 'sqrt'], 'max_depth': [5, 10, 15, 20, 25, 30], 'min_samples_split': [2, 5, 10, 15, 100], 'min_samples_leaf': [1, 2, 5, 10]}\n"
]
}
],
"source": [
"# Create the random grid\n",
"random_grid = {'n_estimators': n_estimators,\n",
" 'max_features': max_features,\n",
" 'max_depth': max_depth,\n",
" 'min_samples_split': min_samples_split,\n",
" 'min_samples_leaf': min_samples_leaf}\n",
"\n",
"print(random_grid)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"# Use the random grid to search for best hyperparameters\n",
"# First create the base model to tune\n",
"rf = RandomForestRegressor()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"# Random search of parameters, using 3 fold cross validation, \n",
"# search across 100 different combinations\n",
"rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid,scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, random_state=42, n_jobs = 1)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
"[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time= 0.5s\n",
"[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time= 0.5s\n",
"[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time= 0.4s\n",
"[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time= 0.4s\n",
"[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time= 0.4s\n",
"[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time= 0.6s\n",
"[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time= 0.6s\n",
"[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time= 0.6s\n",
"[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time= 0.6s\n",
"[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time= 0.6s\n",
"[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time= 0.1s\n",
"[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time= 0.1s\n",
"[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time= 0.1s\n",
"[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time= 0.1s\n",
"[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time= 0.1s\n",
"[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time= 0.2s\n",
"[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time= 0.2s\n",
"[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time= 0.2s\n",
"[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time= 0.2s\n",
"[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time= 0.2s\n",
"[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time= 0.4s\n",
"[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time= 0.4s\n",
"[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time= 0.4s\n",
"[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time= 0.4s\n",
"[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time= 0.4s\n",
"[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time= 0.7s\n",
"[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time= 0.7s\n",
"[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time= 0.7s\n",
"[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time= 0.6s\n",
"[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time= 0.6s\n",
"[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time= 0.6s\n",
"[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time= 0.6s\n",
"[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time= 0.6s\n",
"[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time= 0.6s\n",
"[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time= 0.5s\n",
"[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time= 0.1s\n",
"[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time= 0.1s\n",
"[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time= 0.1s\n",
"[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time= 0.1s\n",
"[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time= 0.1s\n",
"[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time= 0.3s\n",
"[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time= 0.3s\n",
"[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time= 0.3s\n",
"[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time= 0.3s\n",
"[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time= 0.3s\n",
"[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time= 0.4s\n",
"[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time= 0.4s\n",
"[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time= 0.4s\n",
"[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time= 0.4s\n",
"[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time= 0.4s\n"
]
},
{
"data": {
"text/plain": [
"RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
" param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
" 'max_features': ['auto', 'sqrt'],\n",
" 'min_samples_leaf': [1, 2, 5, 10],\n",
" 'min_samples_split': [2, 5, 10, 15,\n",
" 100],\n",
" 'n_estimators': [100, 200, 300, 400,\n",
" 500, 600, 700, 800,\n",
" 900, 1000, 1100,\n",
" 1200]},\n",
" random_state=42, scoring='neg_mean_squared_error',\n",
" verbose=2)"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rf_random.fit(x_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'n_estimators': 700,\n",
" 'min_samples_split': 15,\n",
" 'min_samples_leaf': 1,\n",
" 'max_features': 'auto',\n",
" 'max_depth': 20}"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rf_random.best_params_"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-3.8959446708062027"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rf_random.best_score_"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"y_predict=rf_random.predict(x_test)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\User\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x2c7d690a860>"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(y_test-y_predict)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x2c7d7a8b5c0>"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(y_test, y_predict)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Linearity in the scatterplot shows how that model is good"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"from sklearn import metrics"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MAE: 0.725320076435229\n",
"MSE: 1.4111265228590966\n",
"RMSE: 1.1879084656904741\n"
]
}
],
"source": [
"print('MAE:', metrics.mean_absolute_error(y_test, y_predict))\n",
"print('MSE:', metrics.mean_squared_error(y_test, y_predict))\n",
"print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, y_predict)))"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"# open a file, where you ant to store the data\n",
"file = open('random_forest_regression_model.pkl', 'wb')\n",
"\n",
"# dump information to that file\n",
"pickle.dump(rf_random, file)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}