Here is my data analysis project 'CarFinder' made for Microsoft intern engage program 2022. Get all the information about my project here.
When we need to sell our old car or buy a new one, we usually ask our friends, relatives, and agents for the best price, compare prices, and analyze the car market trend. This is a time-consuming and tedious process. Our website 'CarFinder' a data analysis project is the all-in-one solution to this problem.
With our smart car price predictor model, you can receive the best price for your automobile right here. You will also receive all of the necessary information regarding various automobile models. Our website has a 'cities filter' that will assist you to find information specific to your city. There is a clever Power BI sales dashboard that provides all of the information about the car selling market. This is a very useful feature for any sales team, and it distinguishes the website from other websites by making it beneficial for sales teams in the business. There's also a website performance dashboard, which is particularly beneficial for digital marketing teams who can use it to gain a sense of what users are interested in.
For running this project on your system, you need to have VS code editor (or any other favorite code editor), install some necessary libraries.
- Download the VS code from following link and install it in your system
https://code.visualstudio.com/download-
Clone the repo
git clone https://github.com/Gayatri2002/CarFinder-Data-Analysis-Project
-
Installation of libraries Install the following libraries in your system by using given pip commands in terminal.
- Streamlit
pip install streamlit
- pandas
pip install pandas
- numpy
pip install numpy
- pickle
pip install pickle
- plotly.express
pip install plotly.express
- streamlit_option_menu
pip install streamlit_option_menu
- sqlalchemy
pip install sqlalchemy
You can find all other libraries in requirement.txt file in the repository. Do check that. If any other library is required then you can install it easily by using [pip install library-name] format.
We have used pickle, streamlit to implement this project
To deploy this project run
streamlit run app.pyClone the project
git clone https://github.com/Gayatri2002/CarFinder-Data-Analysis-Project.gitGo to the project directory
cd CarFinder-Data-Analysis-ProjectInstall dependencies
pip install streamlit pip install pandas pip install numpy pip install pickle pip install plotly.express pip install streamlit_option_menu pip install sqlalchemyStart the server
streamlit run app.py- Build the car price prediction model with python
- Create app.py file and implement the model
- Write the code for website in app.py file
- Create Dashboards in Power BI
- Publish the dashboards
- Embed the dashboards in website
Datasets are downloaded from Kaggle
Dataset used for sales dashboard
Dataset used for website performance dashboard
Dataset used for pie chart and car info tab
- Responsiveness(fit for both desktop and mobiles)
- Filters to make searching easy
- User friendly and interactive interface
- Interactive Dashboards
- Accurate Car price predictor
Languages Used: Python, HTML
Framework: streamlit
This is a data analysis project that will benefit everyone who wants to sell or purchase a car, as well as sales and digital marketing teams in the automotive sector who want to do research. For sales and digital marketing teams in the automotive business, the car information, sales dashboard, and website performance tab from the website are useful. In the following ppt and video the project idea and it's uses are explained.
This project can be used by the following people:
- One who wants to sell or purchase car
- Sales teams in automotive industry
- Digital marketing teams in automotive industry
- Those who want to do have information about cars in their city
- Those who want to get best price for selling their second hand car
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.
Gayatri Kalyani - Linkedin
Mail ID - gayatrikalyani44@gmail.com