Colab Product Vector Product

GitHub Repo https://github.com/Akshitha-Maroju/sentiment-analysis-product-reviews

Akshitha-Maroju/sentiment-analysis-product-reviews

A machine learning-based product review rating system that takes user reviews as input and predicts a numerical rating on a scale of 1 to 5. Implemented using Python, pandas, scikit-learn, and TF-IDF vectorization in Google Colab. The model is deployed with a simple web interface for real-time predictions.

GitHub Repo https://github.com/srideepkar/FORECASTING-ASSESSMENT-AND-ANALYSIS-OF-ECONOMIC-CLIMATE-OF-INDIA-A-PROJECT-REPORT

srideepkar/FORECASTING-ASSESSMENT-AND-ANALYSIS-OF-ECONOMIC-CLIMATE-OF-INDIA-A-PROJECT-REPORT

GDP or Gross Domestic Product is the most important economic factor for the whole world. The whole economic climate is mainly depending on the Gross Domestic Product or GDP. It is the measure of economic growth of the country. It defines the money value of the final goods and services produced within the geographical boundary of a country in a year. As an example, the advertisement of products in news paper comes and when people buy those products from the stores then the GDP increased and also when people pay doctors fee, mechanic’s fee, i.e. when each and every transaction the GDP increases. But for some of reasons like disasters, destruction, theft etc causes the decreasing of GDP. In this project we have used three powerful algorithms to calculate and predict the gross domestic product of India. The algorithms are linear logistic regression, random forest and SVM or support vector machine. And the whole proposed word is done into the google colab. The data set used in this project contains 126 rows and 6 columns which defines the year, population, literacy rate, import, export and GDP of India from year 1897 to 2020.

GitHub Repo https://github.com/suy1968/Movie_Recommendation-Engine

suy1968/Movie_Recommendation-Engine

Movie_Recommendation-Engine Introduction: Recommender System are widely used today in all most all the applications.The purpose of a recommender system is to suggest users something based on their interest or usage history.Two most ubiquitous types of personalized recommendation systems are Content-Based and Collaborative Filtering. Collaborative filtering produces recommendations based on the knowledge of users� attitude to items, that is it uses the �wisdom of the crowd� to recommend items. In contrast, content-based recommendation systems focus on the attributes of the items and give you recommendations based on the similarity between them. We have created a Recommender sysem using Spotify We have Scrapped dataset from SPOTIFY using our custom scraper, "Scrapify". The Scrapped data is converted to as csv file and used for further processing.The dataset contains appromixately 11k observations Data Description: -name : Name of the user -artist : Name of the artist -danceability : Ranges from 0 to 1 -key : Ranges from 0 to 11 -mode : Ranges from 0 and 1 -instrumentalness : Ranges from 0 to 1 -duration : Duration of the song in minutes -energy : Ranges from 0 to 1 -loudness : Float typically ranging from -60 to 0 -speechiness : Ranges from 0 to 1 -acousticness : Ranges from 0 to 1 -tempo : Float typically ranging from 0 to 150 -liveness : Ranges from 0 to 1 -valence : Ranges from 0 to 1 -popularity : Ranges from 0 to 100 -hollywood : Hollywood song 1 | Bollywood song 0 Project Goals The goals for this project are: -Scrap the website and collect the required data -Organise the data into a Structured format Gather insights from data analysis about the columns used Perform EDA and remove unwanted columns -Use the Cosine Similarity to calculate a numeric quantity that denotes the similarity between two songs. Since we have used the vectors, calculating the Dot Product will directly give us the Cosine Similarity Score. Ouput the top 5 recommended songs Technologies Used: Python Google Colab Spotify API & custom scraper