Content Based Recommender System recommends movies similar to the movie user likes and analyses the sentiments on the reviews given by the user for that movie.
The details of the movies(title, genre, runtime, rating, poster, etc) are fetched using an API by TMDB, https://www.themoviedb.org/documentation/api, and using the IMDB id of the movie in the API.
used web scraping to get the reviews given by the user in the IMDB site using beautifulsoup4 and performed sentiment analysis on those reviews.
To run tests, run the following command
python app.pyTo run a Heroku deployment tests, click on the following link:
Netflix Recommender System App
1. Data Extraction
2. Exploratory Data Analysis(EDA)
3. Feature Engineering
4. Model Building and Tuning
5. Building Flask API
6. Pushing code to Github
7. Connecting to your Heroku account
8. Deploy App
To install the libraries used in this project. Follow the below steps:
import numpy as np
import pandas as pd
from flask import Flask, render_template, request
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import json
import bs4 as bs
import urllib.request
import pickle
import requests
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