Car Rate Prediction

This dataset is used for price prediction of car, this dataset includes information about cars and motorcycles listed on CarDekho.com. The data is in a CSV file which includes the following columns: model, year, selling price, showroom price, kilometers driven, fuel type, seller type, transmission, and number of previous owners.

We will create a model with the following steps:

Download and explore the dataset
Prepare the dataset for training
Create a linear regression model
Train the model to fit the data
Make predictions using the trained model

This assignment builds upon the concepts from the first 2 lectures. It will help to review these Jupyter notebooks:

PyTorch basics: https://jovian.ml/aakashns/01-pytorch-basics
Linear Regression: https://jovian.ml/aakashns/02-linear-regression
Logistic Regression: https://jovian.ml/aakashns/03-logistic-regression
Linear regression (minimal): https://jovian.ml/aakashns/housing-linear-minimal
Logistic regression (minimal): https://jovian.ml/aakashns/mnist-logistic-minimal

# Uncomment and run the commands below if imports fail
#!conda install numpy pytorch torchvision cpuonly -c pytorch -y --quiet
#!pip install matplotlib --upgrade --quiet
!pip install jovian --upgrade --quiet
#!pip install pandas --upgrade --quiet

import torch
import jovian
import torchvision
import torch.nn as nn
import pandas as pd
import matplotlib.pyplot as plt
import torch.nn.functional as F
from torchvision.datasets.utils import download_url
from torch.utils.data import DataLoader, TensorDataset, random_split
import matplotlib
import seaborn as sns

project_name='car-rate-prediction' # will be used by jovian.commit

Step 1: Download and explore the data

Let us begin by downloading the data. We'll use the download_url function from PyTorch to get the data as a CSV (comma-separated values) file.