{ "cells": [ { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " | property_type | \n", "price | \n", "location | \n", "city | \n", "baths | \n", "purpose | \n", "bedrooms | \n", "Area_in_Marla | \n", "
---|---|---|---|---|---|---|---|---|
108839 | \n", "House | \n", "13800000 | \n", "Pak Arab Housing Society | \n", "Lahore | \n", "3 | \n", "For Sale | \n", "3 | \n", "5.0 | \n", "
97355 | \n", "House | \n", "17500000 | \n", "Marghzar Officers Colony | \n", "Lahore | \n", "6 | \n", "For Sale | \n", "6 | \n", "10.0 | \n", "
125129 | \n", "House | \n", "12500000 | \n", "Adiala Road | \n", "Rawalpindi | \n", "5 | \n", "For Sale | \n", "5 | \n", "10.0 | \n", "
155467 | \n", "Lower Portion | \n", "47000 | \n", "Satellite Town | \n", "Rawalpindi | \n", "3 | \n", "For Rent | \n", "3 | \n", "7.0 | \n", "
81132 | \n", "House | \n", "7800000 | \n", "Shalimar Housing Scheme | \n", "Lahore | \n", "4 | \n", "For Sale | \n", "3 | \n", "4.0 | \n", "
... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
122491 | \n", "House | \n", "19000000 | \n", "Lake City | \n", "Lahore | \n", "5 | \n", "For Sale | \n", "4 | \n", "10.0 | \n", "
44101 | \n", "Upper Portion | \n", "40000 | \n", "Korang Town | \n", "Islamabad | \n", "5 | \n", "For Rent | \n", "4 | \n", "20.0 | \n", "
99634 | \n", "House | \n", "42500000 | \n", "DHA Defence | \n", "Lahore | \n", "5 | \n", "For Sale | \n", "4 | \n", "10.0 | \n", "
147606 | \n", "Flat | \n", "6800000 | \n", "Bahria Town Karachi | \n", "Karachi | \n", "2 | \n", "For Sale | \n", "2 | \n", "4.2 | \n", "
65106 | \n", "House | \n", "300000 | \n", "F-7 | \n", "Islamabad | \n", "6 | \n", "For Rent | \n", "6 | \n", "22.0 | \n", "
10000 rows × 8 columns
\n", "\n", " | property_type | \n", "price | \n", "location | \n", "city | \n", "baths | \n", "purpose | \n", "bedrooms | \n", "Area_in_Marla | \n", "
---|---|---|---|---|---|---|---|---|
108839 | \n", "House | \n", "13800000 | \n", "Pak Arab Housing Society | \n", "Lahore | \n", "3 | \n", "For Sale | \n", "3 | \n", "5.0 | \n", "
97355 | \n", "House | \n", "17500000 | \n", "Marghzar Officers Colony | \n", "Lahore | \n", "6 | \n", "For Sale | \n", "6 | \n", "10.0 | \n", "
125129 | \n", "House | \n", "12500000 | \n", "Adiala Road | \n", "Rawalpindi | \n", "5 | \n", "For Sale | \n", "5 | \n", "10.0 | \n", "
155467 | \n", "Lower Portion | \n", "47000 | \n", "Satellite Town | \n", "Rawalpindi | \n", "3 | \n", "For Rent | \n", "3 | \n", "7.0 | \n", "
81132 | \n", "House | \n", "7800000 | \n", "Shalimar Housing Scheme | \n", "Lahore | \n", "4 | \n", "For Sale | \n", "3 | \n", "4.0 | \n", "
... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
122491 | \n", "House | \n", "19000000 | \n", "Lake City | \n", "Lahore | \n", "5 | \n", "For Sale | \n", "4 | \n", "10.0 | \n", "
44101 | \n", "Upper Portion | \n", "40000 | \n", "Korang Town | \n", "Islamabad | \n", "5 | \n", "For Rent | \n", "4 | \n", "20.0 | \n", "
99634 | \n", "House | \n", "42500000 | \n", "DHA Defence | \n", "Lahore | \n", "5 | \n", "For Sale | \n", "4 | \n", "10.0 | \n", "
147606 | \n", "Flat | \n", "6800000 | \n", "Bahria Town Karachi | \n", "Karachi | \n", "2 | \n", "For Sale | \n", "2 | \n", "4.2 | \n", "
65106 | \n", "House | \n", "300000 | \n", "F-7 | \n", "Islamabad | \n", "6 | \n", "For Rent | \n", "6 | \n", "22.0 | \n", "
9999 rows × 8 columns
\n", "\n", " | city | \n", "location | \n", "Area_in_Marla | \n", "bedrooms | \n", "baths | \n", "
---|---|---|---|---|---|
57800 | \n", "Karachi | \n", "Cantt | \n", "11.4 | \n", "3 | \n", "3 | \n", "
166018 | \n", "Lahore | \n", "Green Cap Housing Society | \n", "3.0 | \n", "4 | \n", "4 | \n", "
150291 | \n", "Karachi | \n", "DHA Defence | \n", "20.0 | \n", "2 | \n", "2 | \n", "
133319 | \n", "Rawalpindi | \n", "Bahria Town Rawalpindi | \n", "5.0 | \n", "3 | \n", "4 | \n", "
119191 | \n", "Lahore | \n", "Canal Garden | \n", "5.0 | \n", "3 | \n", "3 | \n", "
... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
101191 | \n", "Rawalpindi | \n", "Bahria Town Rawalpindi | \n", "7.0 | \n", "5 | \n", "5 | \n", "
48738 | \n", "Lahore | \n", "DHA Defence | \n", "20.0 | \n", "5 | \n", "6 | \n", "
69691 | \n", "Karachi | \n", "Gulshan-e-Iqbal Town | \n", "12.0 | \n", "3 | \n", "3 | \n", "
160108 | \n", "Karachi | \n", "Cantt | \n", "11.4 | \n", "3 | \n", "3 | \n", "
124749 | \n", "Islamabad | \n", "Pakistan Town | \n", "10.0 | \n", "3 | \n", "3 | \n", "
6999 rows × 5 columns
\n", "Pipeline(steps=[('columntransformer',\n", " ColumnTransformer(remainder='passthrough',\n", " transformers=[('onehotencoder',\n", " OneHotEncoder(), ['city']),\n", " ('tfidfvectorizer',\n", " TfidfVectorizer(max_df=0.5,\n", " min_df=5,\n", " ngram_range=(1,\n", " 3)),\n", " 'location'),\n", " ('standardscaler-1',\n", " StandardScaler(),\n", " ['Area_in_Marla']),\n", " ('standardscaler-2',\n", " StandardScaler(),\n", " ['bedrooms']),\n", " ('standardscaler-3',\n", " StandardScaler(),\n", " ['baths'])])),\n", " ('sgdregressor', SGDRegressor(random_state=0))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
Pipeline(steps=[('columntransformer',\n", " ColumnTransformer(remainder='passthrough',\n", " transformers=[('onehotencoder',\n", " OneHotEncoder(), ['city']),\n", " ('tfidfvectorizer',\n", " TfidfVectorizer(max_df=0.5,\n", " min_df=5,\n", " ngram_range=(1,\n", " 3)),\n", " 'location'),\n", " ('standardscaler-1',\n", " StandardScaler(),\n", " ['Area_in_Marla']),\n", " ('standardscaler-2',\n", " StandardScaler(),\n", " ['bedrooms']),\n", " ('standardscaler-3',\n", " StandardScaler(),\n", " ['baths'])])),\n", " ('sgdregressor', SGDRegressor(random_state=0))])
ColumnTransformer(remainder='passthrough',\n", " transformers=[('onehotencoder', OneHotEncoder(), ['city']),\n", " ('tfidfvectorizer',\n", " TfidfVectorizer(max_df=0.5, min_df=5,\n", " ngram_range=(1, 3)),\n", " 'location'),\n", " ('standardscaler-1', StandardScaler(),\n", " ['Area_in_Marla']),\n", " ('standardscaler-2', StandardScaler(),\n", " ['bedrooms']),\n", " ('standardscaler-3', StandardScaler(),\n", " ['baths'])])
['city']
OneHotEncoder()
location
TfidfVectorizer(max_df=0.5, min_df=5, ngram_range=(1, 3))
['Area_in_Marla']
StandardScaler()
['bedrooms']
StandardScaler()
['baths']
StandardScaler()
passthrough
SGDRegressor(random_state=0)
Pipeline(steps=[('columntransformer',\n", " ColumnTransformer(remainder='passthrough',\n", " transformers=[('onehotencoder',\n", " OneHotEncoder(), ['city']),\n", " ('tfidfvectorizer',\n", " TfidfVectorizer(max_df=0.5,\n", " min_df=5,\n", " ngram_range=(1,\n", " 3)),\n", " 'location'),\n", " ('standardscaler-1',\n", " StandardScaler(),\n", " ['Area_in_Marla']),\n", " ('standardscaler-2',\n", " StandardScaler(),\n", " ['bedrooms']),\n", " ('standardscaler-3',\n", " StandardScaler(),\n", " ['baths'])])),\n", " ('sgdregressor',\n", " SGDRegressor(alpha=1e-05, l1_ratio=0.01, penalty='elasticnet',\n", " random_state=0))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
Pipeline(steps=[('columntransformer',\n", " ColumnTransformer(remainder='passthrough',\n", " transformers=[('onehotencoder',\n", " OneHotEncoder(), ['city']),\n", " ('tfidfvectorizer',\n", " TfidfVectorizer(max_df=0.5,\n", " min_df=5,\n", " ngram_range=(1,\n", " 3)),\n", " 'location'),\n", " ('standardscaler-1',\n", " StandardScaler(),\n", " ['Area_in_Marla']),\n", " ('standardscaler-2',\n", " StandardScaler(),\n", " ['bedrooms']),\n", " ('standardscaler-3',\n", " StandardScaler(),\n", " ['baths'])])),\n", " ('sgdregressor',\n", " SGDRegressor(alpha=1e-05, l1_ratio=0.01, penalty='elasticnet',\n", " random_state=0))])
ColumnTransformer(remainder='passthrough',\n", " transformers=[('onehotencoder', OneHotEncoder(), ['city']),\n", " ('tfidfvectorizer',\n", " TfidfVectorizer(max_df=0.5, min_df=5,\n", " ngram_range=(1, 3)),\n", " 'location'),\n", " ('standardscaler-1', StandardScaler(),\n", " ['Area_in_Marla']),\n", " ('standardscaler-2', StandardScaler(),\n", " ['bedrooms']),\n", " ('standardscaler-3', StandardScaler(),\n", " ['baths'])])
['city']
OneHotEncoder()
location
TfidfVectorizer(max_df=0.5, min_df=5, ngram_range=(1, 3))
['Area_in_Marla']
StandardScaler()
['bedrooms']
StandardScaler()
['baths']
StandardScaler()
[]
passthrough
SGDRegressor(alpha=1e-05, l1_ratio=0.01, penalty='elasticnet', random_state=0)