This project involves predicting used car prices using linear regression in Jupyter Notebook. Used car price prediction is an important task in the automotive industry, as it helps estimate the value of pre-owned vehicles based on various factors such as mileage, brand, age, etc.

This project involves the prediction of salary based on position using Support Vector Regression (SVR) in Jupyter Notebook. The dataset contains information about different positions and their corresponding salaries. Through this analysis, we aim to build a regression model that accurately predicts the salary based on the given position.

EDA (Exploratory Data Analysis) -1: Loading the Datasets, Data type conversions,Removing duplicate entries, Dropping the column, Renaming the column, Outlier Detection, Missing Values and Imputation (Numerical and Categorical), Scatter plot and Correlation analysis, Transformations, Automatic EDA Methods (Pandas Profiling and Sweetviz).

Perform Clustering (Hierarchical, K Means Clustering and DBSCAN) for the airlines and crime data to obtain optimum number of clusters. Draw the inferences from the clusters obtained.

Collection of Regression models with maximum accuracy [.98] to predict Dimond price

Project is about predicting Class Of Beans using Supervised Learning Models

Models bank loan applications to classify and predict approval decisions using customer demographic, financial, and loan data. Applies machine learning algorithms like logistic regression and random forest for enhanced automation.

Analyzed 5,000+ movies with Pandas and Colab to build a machine learning model predicting movie revenue.

Unsupervised-ML---K-Means-Clustering-Non-Hierarchical-Clustering-Univ. Use Elbow Graph to find optimum number of clusters (K value) from K values range. The K-means algorithm aims to choose centroids that minimise the inertia, or within-cluster sum-of-squares criterion WCSS. Plot K values range vs WCSS to get Elbow graph for choosing K (no. of c…

This repository contains clustering techniques applied to minute weather data. It contains K-Means, Heirarchical Agglomerative clustering. I have applied various feature scaling techniques and explored the best one for our dataset

Analysis of Terry Stops in Seattle

Prepare a classification model using Naive Bayes for salary data

Perform clustering (K means clustering) for the airlines data to obtain optimum number of clusters. Draw the inferences from the clusters obtained. Data Description: The file EastWestAirlinescontains information on passengers who belong to an airline’s frequent flier program. For each passenger the data include information on their mileage histo…

Predict the Burned Area of Forest Fire with Neural Networks and Predicting Turbine Energy Yield (TEY) using Ambient Variables as Features.

Exploratory Data Analysis Part-1

Assignment-07-K-Means-Clustering-Airlines. Perform clustering (K means clustering) for the airlines data to obtain optimum number of clusters. Draw the inferences from the clusters obtained. The file EastWestAirlinescontains information on passengers who belong to an airline’s frequent flier program. For each passenger the data include informati…

Unsupervised-ML---DBSCAN-Clustering-Wholesale-Customers. Import Libraries, Import Dataset, Normalize heterogenous numerical data using standard scalar fit transform to dataset, DBSCAN Clustering, Noisy samples are given the label -1, Adding clusters to dataset.

This is Date Fruit Data taken from Kaggle. This data severs a classification problem to solved. Using various features of the fruit classify the fruit to its type.

Use Random Forest to prepare a model on fraud data. Treating those who have taxable income <= 30000 as "Risky" and others are "Good" and A cloth manufacturing company is interested to know about the segment or attributes causes high sale.