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linear-algebra

linkedin---interview---question

linkedin-certificate

live-in-labs-water-consumption-survey-data-analyis

The Project Overview : Studied the needs of a rural, backward and socially-isolated village in Northern India in the context of their developmental goals and social aspirations. Established that “Water stress” arising from poor water management practices was a significant hindrance to the village’s development. A human-centered methodology was followed to collect both qualitative as well as quantitative data and the analysis was performed using both computational techniques as well as qualitative methods. The findings were informed to the stakeholders as well as relevant policy-makers.

login-page

machine-algorithm-

machine-learning

machine-learning-algorithms-notebook

machine-learning-basic

machine-learning-prediction-for-graduates-admission-in-universities-python-implementation-

matplotlib

mini-python-projects

THIS CONTAIN MANY SMALL AND CREATIVE PYTHON PROJECTS FOR BEGINNERS

ml-hackaton-checking-

ml-op

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

numpy-and-pandas

BAISC OPERATIONS

online-retail-sales-performance-analysis-report-python-implementation-

opps-and-design-pattern-system-design-

os--fcfs

os--simulation

pandas-kaggle

penguin-classifier-

performing-exploratory-data-analysis-on-pubg-dataset

In a PUBG game, up to 100 players start in each match (matchId). Players can be on teams (groupId) which get ranked at the end of the game (winPlacePerc) based on how many other teams are still alive when they are eliminated. In game, players can pick up different munitions, revive downed-but-not-out (knocked) teammates, drive vehicles, swim, run, shoot, and experience all of the consequences -- such as falling too far or running themselves over and eliminating themselves. You are provided with a large number of anonymized PUBG game stats, formatted so that each row contains one player's post-game stats. The data comes from matches of all types: solos, duos, squads, and custom; there is no guarantee of there being 100 players per match, nor at most 4 players per group.

predicting-zomato-restaurants-ratings

The Zomato restaurants dataset is analyzed to get a fair idea of the factors affecting the aggregate rating of each resurants with the help of machine learning by making a model with the help of classification technique and data visulaization.

prediction-of-employee-turnover-on-hr-data

prediction-on-kyphosis

prediction-on-olympic-medals

price-prediction-gemstone

probability-implementation

projects-task

What is the Annualized Return of the person who bids in the last month ? What is the Annualized Return of the person who bids in the first month ? Write an Python script which calculates the annualized return of chit fund participant ?- Show the Return % for each month's bid winner.