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

Installation

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

!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

  python app.py

🚀 About Me

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

Hi, I'm Akash! 👋

🔗 Links

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