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8 |
9 |
10 | # Aim
11 |
12 | This project aims to predict the Price of an used Car by taking it's Company name, it's Model name, Year of Purchase, and other parameters.
13 |
14 | 
15 |
16 | ## How to use?
17 |
18 | 1. Clone the repository
19 | 2. Install the required packages in "requirements.txt" file.
20 |
21 | Some packages are:
22 | - numpy
23 | - pandas
24 | - scikit-learn
25 |
26 | 3. Run the "application.py" file
27 | And you are good to go.
28 |
29 | # Description
30 |
31 | ## What this project does?
32 |
33 | 1. This project takes the parameters of an used car like: Company name, Model name, Year of Purchase, Fuel Type and Number of Kilometers it has been driven.
34 | 2. It then predicts the possible price of the car. For example, the image below shows the predicted price of our Hyundai Grand i10.
35 |
36 |
37 |
38 | ## How this project does?
39 |
40 | 1. First of all the data was scraped from Quikr.com (https://quikr.com)
41 | Link for data: https://github.com/rajtilakls2510/car_price_predictor/blob/master/quikr_car.csv
42 |
43 | 2. The data was cleaned (it was super unclean :( ) and analysed.
44 |
45 | 3. Then a Linear Regression model was built on top of it which had 0.92 R2_score.
46 |
47 | Link for notebook: https://github.com/rajtilakls2510/car_price_predictor/blob/master/Quikr%20Analysis.ipynb
48 |
49 | 4. This project was given the form of an website built on Flask where we used the Linear Regression model to perform predictions.
50 |
51 |
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