https://github.com/akash1070/predicting-zomato-restaurant-ratings

Perform extensive Exploratory Data Analysis(EDA) on the Zomato Dataset. Building an appropriate Machine Learning Model that will help various Zomato Restaurants to predict their respective Ratings based on certain features deploy the Machine learning model via Flask
https://github.com/akash1070/predicting-zomato-restaurant-ratings

data-analysis extratreesregressor flask linear-regression machine-learning random-forest zomato-bangalore zomato-data-analysis

Last synced: 21 days ago
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Perform extensive Exploratory Data Analysis(EDA) on the Zomato Dataset. Building an appropriate Machine Learning Model that will help various Zomato Restaurants to predict their respective Ratings based on certain features deploy the Machine learning model via Flask

• Host: GitHub
• URL: https://github.com/akash1070/predicting-zomato-restaurant-ratings
• Owner: Akash1070
• Created: 2022-09-14T14:30:55.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-09-14T14:42:24.000Z (over 2 years ago)
• Last Synced: 2024-04-05T11:45:29.211Z (9 months ago)
• Topics: data-analysis, extratreesregressor, flask, linear-regression, machine-learning, random-forest, zomato-bangalore, zomato-data-analysis
• Language: Jupyter Notebook
• Homepage:
• Size: 710 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        

# **End-To-End Deployment of Zomato Restaurant Ratings Using Flask**

 The main agenda of this project is:

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

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

3. Deploy the Machine learning model via Flask that can be used to make live predictions of restaurants ratings

## 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

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import plotly.graph_objs as go

import plotly.offline as py

import seaborn as sns

import matplotlib.ticker as mtick

plt.style.use('fivethirtyeight')

from sklearn.linear_model import LogisticRegression

from sklearn.linear_model import LinearRegression

from sklearn.ensemble import  ExtraTreesRegressor

from sklearn.model_selection import train_test_split

import warnings

warnings.filterwarnings('ignore')

%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