https://github.com/akash1070/deploying-machine-learning-models-via-microsoft-azure

Deploying Flight Price Prediction via Microsoft Azure
https://github.com/akash1070/deploying-machine-learning-models-via-microsoft-azure

azure catboost-model extra-tree-regressor flight-price-prediction hyperparameters lightbgm random-forest-regression randomizedsearchcv xgboost-model

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Deploying Flight Price Prediction via Microsoft Azure

• Host: GitHub
• URL: https://github.com/akash1070/deploying-machine-learning-models-via-microsoft-azure
• Owner: Akash1070
• Created: 2022-09-16T05:10:21.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-09-16T05:34:07.000Z (over 2 years ago)
• Last Synced: 2024-04-05T11:45:28.038Z (9 months ago)
• Topics: azure, catboost-model, extra-tree-regressor, flight-price-prediction, hyperparameters, lightbgm, random-forest-regression, randomizedsearchcv, xgboost-model
• Language: HTML
• Homepage:
• Size: 400 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        

# **Deploying Machine Learning Models via Microsoft Azure**

 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 Data to predict Price based on certain features.

3. Deploy the Machine learning model via Microsoft Azure that can be used to make live predictions of 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 flask

from flask import Flask, request, render_template

from flask_cors import cross_origin

import sklearn

import pickle

import pandas as pd

!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 matplotlib.pyplot as plt

import xgboost as xgb

import cufflinks as cf

import seaborn as sns

```

    

## 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/)

## Tech stack

![Logo](https://businesstoys.in/assets/programs/full-stack-data-science-professional-program/tools.png)

## 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 

## Future Plans 

⚡️ Looking forward to help drive innovations into your company as a Data Scientist

⚡️ Looking forward to offer more than I take and leave the place better than i found