This Python script processes a housing dataset to predict property prices using a Linear Regression model. It starts by importing essential libraries (NumPy, Pandas, Matplotlib, Seaborn, and scikit-learn).

The wholesale distributor is considering changing its delivery service from currently 5 days a week to 3 days a week. However, the distributor will only make this change in delivery service for customers that react positively. How can the wholesale distributor use the customer segments to determine which customers, if any, would reach positively…

Exploratory Data Analysis

This is about Treue Technologies Data science Internship tasks.

ML using NumPy and Pandas

Using SVM to predict whether a customer can retire or not based on his/her features.

Feature Scaling for Machine Learning: Understanding the Difference Btw Normalization&Standardization

The objective is to discover insights into consumer reviews and perform sentiment analysis on the data.

Analyzing and predicting life expectancy of a country based on multiple factors using multiple regression techniques

Study feature scaling.

This machine learning model uses various features to predict the weight category of an individual, such as their height, age, gender, and body measurements. And based on that we can analyze that the individual is Extremely Obese, Obesity, Overweight, Normal, weak, Extreme weak.

This repository contain all the file related to Feature Scaling,Label Encoding and corelation,Outliers Removal etc.in short it contain all files related to data preprocessing.

This project contains dataset of house sale prices for USA. It includes homes sold between May 2019 and May 2020. Goal to determine the market price of a house given a set of features.Analyze and predict housing prices using attributes or features such as square footage, number of bedrooms, number of floors, and so on.

In this i have performed complete feature engineering that is from handling null values, Categorical features upto performing feature scaling on our test_data and train_data.