🩺 Advanced neural network for breast cancer classification using Wisconsin dataset. Analyzes cell nucleus characteristics from FNA samples to distinguish malignant/benign masses with 96.5% accuracy. Features comprehensive documentation, automated setup, testing framework, and deployment guides. Educational ML project with 15,000+ lines of docs.

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.

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.

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.

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).

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

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

ML-powered system for detecting and analyzing psychiatric disorders using statistical modeling and machine learning techniques.

Project is about predicting Class Of Beans using Supervised Learning Models

A Spotify Hit Predictor capable of predicting whether any given track would be a 'Hit' or 'Flop'. By using the Spotify "hit predictor" dataset, with data analysis of past "hits" by decade. Deployment using Flask via Heroku.

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.

This Repo contains the 4th Task for my FreeCodeCamp Course

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 clusters)

Exploratory Data Analysis Part-1

Website Kualitas Jeruk dengan Jupyter Notebook dan Streamlit, menggunakan algoritma LogisticRegression sampai StandardScaler dan OneHotEncoder

Prepare a classification model using Naive Bayes for salary data

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.

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

Flask web app that predicts crop yield using CatBoost, XGBoost, and LSTM models.

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 history and on different ways they accrued or spent miles in the last year. The goal is to try to identify clusters of passengers that have similar characteristics for the purpose of targeting different segments for different types of mileage offers ID --Unique ID Balance--Number of miles eligible for award travel Qual_mile--Number of miles counted as qualifying for Topflight status cc1_miles -- Number of miles earned with freq. flyer credit card in the past 12 months: cc2_miles -- Number of miles earned with Rewards credit card in the past 12 months: cc3_miles -- Number of miles earned with Small Business credit card in the past 12 months: 1 = under 5,000 2 = 5,000 - 10,000 3 = 10,001 - 25,000 4 = 25,001 - 50,000 5 = over 50,000 Bonus_miles--Number of miles earned from non-flight bonus transactions in the past 12 months Bonus_trans--Number of non-flight bonus transactions in the past 12 months Flight_miles_12mo--Number of flight miles in the past 12 months Flight_trans_12--Number of flight transactions in the past 12 months Days_since_enrolled--Number of days since enrolled in flier program Award--whether that person had award flight (free flight) or not

Healthcare data

Credit Card Approval Prediction using Logistic Regression model

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

FastAPI create a machine learning from model iris resful API

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 information on their mileage history and on different ways they accrued or spent miles in the last year. The goal is to try to identify clusters of passengers that have similar characteristics for the purpose of targeting different segments for different types of mileage offers.

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.

Website Kualitas Kopi dengan Jupyter Notebook dan Streamlit, menggunakan algoritma LogisticRegression sampai StandardScaler dan OneHotEncoder

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.

Assignment-07-DBSCAN-Clustering-Crimes. Perform Clustering for the crime data and identify the number of clusters formed and draw inferences.