Automobile-Data-Analysis

CarScrutiny App

CarScrutiny is a project based on Data Analytics that I have developed as a part of Microsoft Engage’22. This web application demonstrates how the Automotive Industry could harness data to make informed decisions.

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Technology Stack:

Backend:Python

Frontend:Streamlit library

Deployed using Streamlit share

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Access the website:

1. Website link: https://share.streamlit.io/sonakshigoyal/automobile-data-analysis/main/app.py

OR

2. Clone the repository and run from terminal using the command: streamlit run app.py

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Libraries Used:

matplotlib==3.5.1

numpy==1.22.0

pandas==1.3.5

scikit_learn==1.1.1

scipy==1.8.1

seaborn==0.11.2

streamlit==1.9.0

streamlit_option_menu==0.3.2

protobuf~=3.19.0

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Features Implemented:

1. Univariate Analysis : Used to find most popular car specification, according to different parameters
2. Bivariate Analysis : Used to visualize and analyse variation of price on the basis of different parameters
3. Regression Analysis : Use price predictor variables to predict price by 3 ML models: Multiple linear regression, Polynomial linear regression, Random Forest regression
4. Price Prediction : By taking input values for predictor parameters predicting the price using Random Forest Regression

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Conclusion:

This web app can be used by Automotive industries to perform various data analysis and improvise their production and sales.