Write_a_Data_Science_Blog_Post

First project as part of Udacity Data Science Nano degree program

Table of Contents:

1. Project Introduction
2. File Description
3. Libraries used
4. Results
5. Licensing, Acknowledgements

Project Introduction

For this proect, i was interested in using dataset from CarDekho on used cars to better understand how the selling price is calculated. I analyzed the following:

1. What is the age of the car when selling price is the best?

2. Do we sell directly or through a dealer? Does first hand car get more selling price than 2nd/3rd hand etc..

3. Understand the important factors behind selling price using Machine Learning Models.

File Description

There are two files attached in this repository. First is the excel file, which is the dataset from Kaggle (https://www.kaggle.com/nehalbirla/vehicle-dataset-from-cardekho) and second is the jupiter note book file with the entire analysis answering above questions

Libraries Used

Following Python libraries were used for Exploratory data analysis & modeling.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import datetime
import statsmodels.api as sm
import seaborn as sns
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score, mean_squared_error
from sklearn.impute import KNNImputer
from matplotlib.ticker import FuncFormatter
%matplotlib inline

Results

The main findings of the code can be found at the post available here.

Licensing, Acknowledgements

Must give credit to CarDekho for the data & the same being available on Kaggle.