I have made this model which will predict estimated price of old car base on thier features. As now a day we know many people are going to buy second hand car instaed of buying new one, so its better investment option where we get almost 30-40% discount. but main question is how will us know actual price of car base on their features so in orer to solve this problem I have used this dataset to build model which will give a estimated price of car at which car should be sold.
This Dataset contains information of 5000+ old cars with different models and features like their Year, Name of the Company, KM driven, Power, Fuel Type and Location. This Dataset contains total 12 features
- Name
- Location
- Year
- Kilometers_Driven
- Fuel_Type
- Transmission
- Owner_Type
- Mileage
- Engine
- Power
- Seats
- Price
- I have made this model which will predict estimated price of old car base on thier features such as brand,KM drive,Power,Year and so on..
- I have done stepwise EDA (Exploratory Data Analysis) then visualizatiion to get some idea about important features or correlation of each feature with output which dominates more to predict price
- Then I have done Feature Engineering which inclueds features extraction & features construction based on my domian knowledge and visualization followed by label encoding
- I have train multiples ML models on same data in order to Analysed & compare performance of differents models based of accuracy and complexity
- I have used all regression algorithms to train model and after comparing I got well accuracy by RandomForestRegressor after cross validation which was around 90%
- Finally Build web application in python using streamlit library and then deploy the model
- https://karanchinch10-oldcar-sell-streamlit-app-p6gwqq.streamlitapp.com/ works too. Must be used for explicit links.
- Technical tools or library used --Python,numpy,pandas,sklearn,matplotllib,html,css,streamlit
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- 👉View on Kaggle 💝
- 👉View On Github 💝