Problem Statement - We were given certain data about cars , and we had to predict the popularity of the cars.
Data to predict – popularity (of cars)
Predictors –
- buying_price
- Maintainance_cost
- number_of_doors
- number_of_seats
- luggage_boot_size
- safety_rating
To preprocess the data open data_preprocess.py , this will help in finding all kind of plots , finding the number of null values , and feature engineering.
For finding the model that would fit this dataset and produce the output csv file go to model_fit.py and run it. The output csv file will be generated in the folder where model_fit.py is present.
Output - Prediction is the file which contains the output which was submitted.
Libraries Used:
matplotlib : https://matplotlib.org/api/pyplot_api.html
pandas : https://pandas.pydata.org/
sklearn SVM : http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html
Made By:Abhinav Srivastava