A predictive model to get the resell value in rupees of any bike in India given some features like brand,mileage power etc.
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An android application has been developed for demo using Flutter.
Android apk file - https://github.com/ropali/whats_my_bike_worth/releases
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The same app has been converted to web app as well.Deployed on heroku, find the demo here https://whatsmybikeworth.herokuapp.com
├── LICENSE
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ and `-` delimited description, e.g.
│ `1.0-initial-data-exploration`.
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, csv etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
Clone this repo in your local machine. Create a virtual environment to install the packages like this,
> virtualenv venv
After it successfully creates virtual environment then activate it.
> source venv/bin/activate # for linux
> .\venv\Scripts\actiavet # for windows
Now install all the require packages from requirements.txt file like this,
> pip install -r requirements.txt
This repo already contains the data in data/raw/data.csv which you can directly use. Or you can scrape the data directly from the source using the script.The orinal way to generate a dataset is to collect a data from source and store it in the sqlite database & then export the dataset into .csv file.
To scrape the data from source then run this command.
> python -m src.data.make_dataset
It will check for local sqlite database and if could not find then will ask you start the scrapping.
To know about the dataset follow this document references/DATA.md
To train the model you can start the training by using this command,
> python -m src.models.train_model
All the in between steps like pre-processing,feature engineering, outlier removal will be performed automatically. All the generated models will be saved in models directory.
The model will be trained on the data using various algorithms. After the training finishes all the models performance will be compared and the model with best R2 score will selected. It will also give the option to perform automatic hyper parameters tuning on the best model.
Following algoriths are used for training.
- LinearRegression
- KNeighborsRegressor
- RandomForestRegressor
- GradientBoostingRegressor
You can add more algorithms by editing the models/model_factory.py file.
To generate basic visualization for the dataset, you can use this command.
> python -m src.visualization.visualize
All the generated figures will be stored in visualizations directory.
All the operations will be logged in the debug.log file which will be generated automatically once you start running the code.