TRAV-BUD:oncoming_automobile: - Data Analysis Track

TRAV-BUD is a data analytics web app that helps the client to solve their queries with the power of automobile data. Using TRAV-BUD Automotive Industry could harness data to take informed decisions.

Why is there need of TRAV-BUD ?

• For automobiles, data may be quite useful in making informed business decisions. Hyundai, for example, has been investing in Big Data since 2012, and they are considered one of the forerunners in understanding its potential and implementing it before anyone else.
• Data will be a crucial enabler as new goods and marketplaces become more competitive, allowing automotive firms to make educated decisions on everything from product portfolios and technology adoption to pricing, target market selection, and supply chain planning.

Dataset Insights -

• The quikr-cars-scraped dataset is an open source dataset with columns like name, company, Price, kms driven, year etc.

Functional Requirements -

• Car Price Predictor
• Carbon Emission Predictor
• Car Launch Year Predictor
• Km Driven Predictor

FLOWCHART

• This flowchart highlights the steps from data preprocessing, cleaning, model building, training and testing to integration and deployment.

FEATURES AND INTERFACES 🖥️

LANDING PAGE

• Seamless landing page with navigation bar, features and services, and footer.

Car Price Predictor

• Predict the Car Price by writing the paramters given in the form.

Carbon Emission Predictor

• Predict the Carbon Dioxide Emission by writing the paramters given in the form.

Car Lauch Year Predictor

• Predict the Car Launch Year by writing the paramters given in the form.

Km Driven Predictor

• Predict the km's driven by writing the paramters given in the form.

Timeline

• Different challenges that I faced all through these 4 weeks, how I overcame them and what I learnt from those mistakes.

TECH STACK

• HTML/CSS/JS/BOOTSTRAP : Frontend Development and Styling
• Python: Building Machine Learning Model
• Flask : Machine Learning Web App Integration
• Heroku : Deployment

Instructions to host locally

1. git clone https://github.com/tishachawla-jg/Microsoft_Engage_22_Trav_bud.git

2. cd ./Microsoft_Engage_22_Trav_bud

3. Install Flask in the virtual environment : python -m pip install flask

4. Run the flask app using : python -m flask run

5. The app is running on http://127.0.0.1:5000/ (Press CTRL+C to quit)

Need help?

Feel free to contact me on LinkedIn

if (youEnjoyed) {

starThisRepository();

}