https://github.com/akash1070/flight-price-predicting---heroku

Flight Price Prediction Model Deployment IN Heroku
https://github.com/akash1070/flight-price-predicting---heroku

catboost-model extra-tree-regressor flight-price-prediction heroku-deployment hyperparameter-optimization lightgbm-models machine-learning random-forest-regression randomizedsearchcv xgboost-model

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Flight Price Prediction Model Deployment IN Heroku

• Host: GitHub
• URL: https://github.com/akash1070/flight-price-predicting---heroku
• Owner: Akash1070
• Created: 2022-09-15T06:21:36.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-09-15T08:29:09.000Z (over 2 years ago)
• Last Synced: 2024-04-05T11:45:27.971Z (9 months ago)
• Topics: catboost-model, extra-tree-regressor, flight-price-prediction, heroku-deployment, hyperparameter-optimization, lightgbm-models, machine-learning, random-forest-regression, randomizedsearchcv, xgboost-model
• Language: Jupyter Notebook
• Homepage:
• Size: 1.31 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        

# **End-To-End Deployment of Flight Price predictions Using Heroku**

 The main agenda of this project is:

1. Perform extensive Exploratory Data Analysis(EDA) on the Airline Dataset.

2. Build an appropriate Machine Learning Model that will help various Airline to predict their respective Price based on certain features

3. Deploy the Machine learning model via Heroku that can be used to make live predictions of Flight Price.

## Authors

- [@Akash Kumar Jha](https://github.com/Akash1070)

## Installation

To install the libraries used in this project. Follow the 

below steps:

```bash

!pip install cufflinks

!pip install chart_studio

!pip install pandas-profiling

from chart_studio.plotly import plot,iplot

from sklearn.ensemble import ExtraTreesRegressor

from sklearn.model_selection import train_test_split

from sklearn.ensemble import  ExtraTreesRegressor

from sklearn.ensemble import RandomForestRegressor

from sklearn.model_selection import RandomizedSearchCV

from sklearn.metrics import mean_absolute_error,mean_squared_error

from catboost import CatBoostRegressor

from lightgbm import LGBMRegressor

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import xgboost as xgb

import cufflinks as cf

import seaborn as sns

import pickle 

%matplotlib inline

```

    

## Running Flask Api

To run tests, run the following command

```bash

  python app.py

```

## 🚀 About Me

Data Scientist Enthusiast | Petroleum Engineer Graduate | Solving Problems Using Data 

# Hi, I'm Akash! 👋

## 🔗 Links

[![github](https://img.shields.io/badge/github-000?style=for-the-badge&logo=ko-fi&logoColor=white)](https://github.com/Akash1070)

[![linkedin](https://img.shields.io/badge/linkedin-0A66C2?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/akashkumar107/)

## Other Common Github Profile Sections

👩‍💻 I’m interested in Petroleum Engineering

🧠 I’m currently learning Data Scientist | Data Analytics | Business Analytics

👯‍♀️ I’m looking to collaborate on Ideas & Data

## 🛠 Skills

1. Data Scientist

2. Data Analyst

3. Business Analyst

4. Machine Learning