/Deploying-Machine-Learning-Models-via-Microsoft-Azure

Deploying Flight Price Prediction via Microsoft Azure

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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

Installation

To install the libraries used in this project. Follow the below steps:

!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

  python app.py

🚀 About Me

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

Hi, I'm Akash! 👋

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