• Demo
• Overview
• Motivation
• Installation
• Deployement on Heroku
• Directory Tree
• Technical Aspect
• Technologies Used
• Team
• Future scope of project

Link: https://flightpricetest.herokuapp.com/

This is a Flask web app which predicts fare of Flight ticket.

As a Data Science enthusiast I am utilizing the lockdown period with implementing various machine learning and deep learning projects and try to do end to end projects,also writing blogs on my data science projects on medium (Link: https://medium.com/analytics-vidhya/implementing-lstm-for-stock-price-prediction-time-series-4c0c65bb493d/)

The Code is written in Python 3.6.10. If you don't have Python installed you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip. To install the required packages and libraries, run this command in the project directory after cloning the repository:

pip install -r requirements.txt

Login or signup in order to create virtual app. You can either connect your github profile or download ctl to manually deploy this project.

Our next step would be to follow the instruction given on Heroku Documentation to deploy a web app.

├── datasets
│   ├── Flight_test.xlsx
│   ├── Flight_train.xlsx
├── static 
│   ├── style.css
├── template
│   ├── home.html
├── Procfile
├── README.md
├── app.py
├── Project_17.ipynb
├── flight_rf.pkl
├── requirements.txt

1. Training a machine learning model.
2. Building and hosting a Flask web app on Heroku.
3. Used Multiple algorithms to train the model.
4. Used Hyperparameter tunning to enchance the performance of model.
5. Done visualization using libraries seaborn, matplotlib and plot univariate,bivariate graphs.
6. Done Feature Selection and EDA(data exploration).

Mayank Yogi

• Use multiple Algorithms
• Optimize Flask app.py
• Front-End