pratik0502 Goto Github PK

awp-ipl-project

IPL Data Wrangling

car_price_prediction

Taking the car price data by using machine learning dealing with outliers and applying multiple linear algorithm and using regularization Methods to Predict the prices of car.

chatbot

complete-case-analysis-cca-for-data-science-job-dataset

Complete Case Analysis (CCA) for Handling Missing Data in "data_science_job.csv" dataset. Approximately 89% data retained. Impact on variables analyzed. Valuable insights gained.

employee-attrition-prediction-with-randomized-grid-search-and-decision-trees

This repository contains code and data for a decision tree-based model that predicts employee attrition. The model utilizes employee attributes like job role, department, salary, and performance ratings. It also demonstrates the use of Randomized Grid Search to optimize hyperparameters, resulting in improved predictive accuracy.

enhancing-the-user-experience-through-database-replication-a-case-study-of-myntra-shopping-site

The case study focuses on the replication of the database of Myntra, an online shopping site. The goal was to create a database replication for the site that could perform various functions. SQL queries were written for new registration with either an email or phone number, password reset for customers who forget their password, searching for prod

exploring-feature-transformation-techniques-for-titanic-classification-binning-and-beyond

This project explores the impact of feature transformation techniques, including binning, on the classification accuracy of the Titanic dataset. By analyzing and comparing the results, we gain insights into the effectiveness of these techniques and their potential for improving classification accuracy.

exploring-supermarket-sales-through-comprehensive-eda

This GitHub project is an exploratory data analysis (EDA) of supermarket sales. The data was gathered and the EDA was performed using Python and the libraries seaborn and matplotlib. The steps involved in the EDA are also documented in the project.

flight-price-prediction-model-using-machine-learning

n this project, we have collected a dataset containing information about flights, including airlines, departure time, arrival time, source city, destination city, duration, stops, class, and price. We aim to build a regression model that can predict the flight prices accurately based on these features.

flipkart-television-web-scraping

Scrape TV names and prices from Flipkart using Python, Requests, and BeautifulSoup. Automate data extraction, save to CSV for easy analysis.

global-drug-supply-chain-a-comprehensive-analysis-of-deliveries-companies-diseases-and-expenditu

Drug Supply Chain Analysis: An interactive dashboard and 10 charts exploring delivery timeliness, distribution, companies, modes of transport, disease imports, top vendors, expenditures, and project management from 2006 to 2015.

iris-flower-species-classification-using-ensemble-learning

In this project, we use the Iris dataset, which contains measurements of four features (sepal length, sepal width, petal length, and petal width) for three different species of Iris flowers (Setosa, Versicolor, and Virginica). The dataset is preprocessed by removing the 'Id' column and encoding the 'Species' column using label encoding.

iris-flower-species-classification-using-k-nearest-neighbors

In this project, we use the Iris dataset, which contains measurements of 150 iris flowers from three different species: setosa, versicolor, and virginica. We begin by loading the dataset and performing some initial data exploration.

machine-learning-on-digits-dataset

Description: Explore various ML models, perform hyperparameter tuning, and evaluate their performance on the Digits dataset—a popular collection of grayscale hand-drawn digits (0-9). Aim to showcase model comparisons and identify the best-performing model.

machine-learning-project-predicting-titanic-survival-with-pipelines-and-imputers

Description: Predict survival status of Titanic passengers using ML techniques. Build accurate model with pipelines and various imputers for missing data. Preprocess, scale features, encode categories, and train predictive model.

nlp-app

My Python-based NLP app includes sentiment analysis, named entity recognition, and emotion detection, with login/signup and JSON authentication.

open_ai-chatbot

optimizing-real-estate-predictions-through-machine-learning-regression-and-svm-model-tuning

A project that uses machine learning regression and SVM model optimization to accurately predict real estate prices based on preprocessed and cleaned data, statistical analysis, and hyper-parameter tuning, resulting in a user-friendly prediction function with an accuracy of 86%.

sms-spam-classification-using-machine-learning

This project builds a machine learning model to classify SMS messages as spam or ham. It involves data preprocessing, analysis, and model building using different classifiers. The aim is to enhance user experience by filtering unwanted messages and improving messaging security.

stockx-sneakers-recommendation-system

his project involves a web crawler to extract data from stocks sneakers. The data is processed and cleaned, and a recommendation system is built based on cosine similarity. Users can input a shoe and receive recommendations of similar shoes. Processed data and similarity matrix are saved for future use.