diff --git a/pages/car_price_predictor.py b/pages/car_price_predictor.py
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+# -*- coding: utf-8 -*-
+"""car_price_predictor.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1rnMmmgkB7PlOcx__mXoXdJ9HYTBxE_TP
+
+# Car Price Predictor
+"""
+
+# Commented out IPython magic to ensure Python compatibility.
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+# %matplotlib inline
+mpl.style.use('ggplot')
+
+car=pd.read_csv('/content/drive/MyDrive/Data Sets/car.csv')
+
+car.head()
+
+car.shape
+
+car.info()
+
+"""##### Creating backup copy"""
+
+backup=car.copy()
+
+car['year'].unique()
+
+car['Price'].unique()
+
+car['kms_driven'].unique()
+
+car['fuel_type'].unique()
+
+"""## Quality
+
+- names are pretty inconsistent
+- names have company names attached to it
+- some names are spam like 'Maruti Ertiga showroom condition with' and 'Well mentained Tata Sumo'
+- company: many of the names are not of any company like 'Used', 'URJENT', and so on.
+- year has many non-year values
+- year is in object. Change to integer
+- Price has Ask for Price
+- Price has commas in its prices and is in object
+- kms_driven has object values with kms at last.
+- It has nan values and two rows have 'Petrol' in them
+- fuel_type has nan values
+
+## Cleaning Data
+
+#### year has many non-year values
+"""
+
+car=car[car['year'].str.isnumeric()]
+
+"""#### year is in object. Change to integer"""
+
+car['year']=car['year'].astype(int)
+
+"""#### Price has Ask for Price"""
+
+car=car[car['Price']!='Ask For Price']
+
+"""#### Price has commas in its prices and is in object"""
+
+car['Price']=car['Price'].str.replace(',','').astype(int)
+
+"""####  kms_driven has object values with kms at last."""
+
+car['kms_driven']=car['kms_driven'].str.split().str.get(0).str.replace(',','')
+
+"""#### It has nan values and two rows have 'Petrol' in them"""
+
+car=car[car['kms_driven'].str.isnumeric()]
+
+car['kms_driven']=car['kms_driven'].astype(int)
+
+"""#### fuel_type has nan values"""
+
+car=car[~car['fuel_type'].isna()]
+
+car.shape
+
+"""### name and company had spammed data...but with the previous cleaning, those rows got removed.
+
+#### Company does not need any cleaning now. Changing car names. Keeping only the first three words
+"""
+
+car['name']=car['name'].str.split().str.slice(start=0,stop=3).str.join(' ')
+
+"""#### Resetting the index of the final cleaned data"""
+
+car=car.reset_index(drop=True)
+
+"""## Cleaned Data"""
+
+car
+
+car.to_csv('Cleaned_Car_data.csv')
+
+car.info()
+
+car.describe(include='all')
+
+"""### Checking relationship of Company with Price"""
+
+car['company'].unique()
+
+import seaborn as sns
+
+plt.subplots(figsize=(15,7))
+ax=sns.boxplot(x='company',y='Price',data=car)
+ax.set_xticklabels(ax.get_xticklabels(),rotation=40,ha='right')
+plt.show()
+
+"""### Checking relationship of Year with Price"""
+
+plt.subplots(figsize=(20,10))
+ax=sns.swarmplot(x='year',y='Price',data=car)
+ax.set_xticklabels(ax.get_xticklabels(),rotation=40,ha='right')
+plt.show()
+
+"""### Checking relationship of kms_driven with Price"""
+
+sns.relplot(x='kms_driven',y='Price',data=car,height=7,aspect=1.5)
+
+"""### Checking relationship of Fuel Type with Price"""
+
+plt.subplots(figsize=(14,7))
+sns.boxplot(x='fuel_type',y='Price',data=car)
+
+"""### Relationship of Price with FuelType, Year and Company mixed"""
+
+ax=sns.relplot(x='company',y='Price',data=car,hue='fuel_type',size='year',height=7,aspect=2)
+ax.set_xticklabels(rotation=40,ha='right')
+
+"""### Extracting Training Data"""
+
+X=car[['name','company','year','kms_driven','fuel_type']]
+y=car['Price']
+
+X
+
+y.shape
+
+"""### Applying Train Test Split"""
+
+from sklearn.model_selection import train_test_split
+X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)
+
+from sklearn.linear_model import LinearRegression
+
+from sklearn.preprocessing import OneHotEncoder
+from sklearn.compose import make_column_transformer
+from sklearn.pipeline import make_pipeline
+from sklearn.metrics import r2_score
+
+"""#### Creating an OneHotEncoder object to contain all the possible categories"""
+
+ohe=OneHotEncoder()
+ohe.fit(X[['name','company','fuel_type']])
+
+"""#### Creating a column transformer to transform categorical columns"""
+
+column_trans=make_column_transformer((OneHotEncoder(categories=ohe.categories_),['name','company','fuel_type']),
+                                    remainder='passthrough')
+
+"""#### Linear Regression Model"""
+
+lr=LinearRegression()
+
+"""#### Making a pipeline"""
+
+pipe=make_pipeline(column_trans,lr)
+
+"""#### Fitting the  model"""
+
+pipe.fit(X_train,y_train)
+
+y_pred=pipe.predict(X_test)
+
+"""#### Checking R2 Score"""
+
+r2_score(y_test,y_pred)
+
+scores=[]
+for i in range(1000):
+    X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.1,random_state=i)
+    lr=LinearRegression()
+    pipe=make_pipeline(column_trans,lr)
+    pipe.fit(X_train,y_train)
+    y_pred=pipe.predict(X_test)
+    scores.append(r2_score(y_test,y_pred))
+
+np.argmax(scores)
+
+scores[np.argmax(scores)]
+
+pipe.predict(pd.DataFrame(columns=X_test.columns,data=np.array(['Maruti Suzuki Swift','Maruti',2019,100,'Petrol']).reshape(1,5)))
+
+"""#### The best model is found at a certain random state"""
+
+X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.1,random_state=np.argmax(scores))
+lr=LinearRegression()
+pipe=make_pipeline(column_trans,lr)
+pipe.fit(X_train,y_train)
+y_pred=pipe.predict(X_test)
+r2_score(y_test,y_pred)
+
+import pickle
+
+pickle.dump(pipe,open('LinearRegressionModel.pkl','wb'))
+
+pipe.predict(pd.DataFrame(columns=['name','company','year','kms_driven','fuel_type'],data=np.array(['Maruti Suzuki Swift','Maruti',2019,100,'Petrol']).reshape(1,5)))
+
+pipe.steps[0][1].transformers[0][1].categories[0]
+