All Data Science and Machine Learning Projects that I have done will avaliable here.

🚀 Breast Cancer Prediction Using R: A Comprehensive Analysis 🌟

🛠️ Project Overview In this project, I implemented various techniques to analyze and predict breast cancer outcomes using the K-Nearest Neighbors (KNN) algorithm.

🔍 Data Analysis and Visualization Data Preparation:

Loaded and processed the dataset, transforming categorical diagnosis labels into factors for analysis. Visualization:

Pie Chart: Illustrated the distribution of benign and malignant diagnoses. Histogram: Showed the distribution of the normalized area_mean feature. Pair Plot: Visualized relationships between selected features and diagnosis categories. Data Normalization:

Normalized the dataset features to prepare for model training.

🔬 Model Training and Evaluation Train-Test Split:

Divided the data into training and testing sets. KNN Algorithm:

Applied the KNN algorithm to predict diagnoses. Performance Evaluation:

Cross Table: Assessed the model's performance in terms of prediction accuracy. Confusion Matrix: Provided insights into true positives, false positives, true negatives, and false negatives. Accuracy: 98% Sensitivity: 100% Specificity: 91.3% Kappa: 0.9418

📊 Tools and Libraries Utilized libraries including ggplot2, caret, class, gmodels, lattice, and GGally for visualization and analysis.

🌟 Exploring Naive Bayes Classification with the Iris Dataset in R 🌟

I'm excited to share my recent project where I implemented the Naive Bayes classification algorithm using Iris dataset! 🌸

📊 Highlights:

Dataset: The Iris dataset consists of 150 samples from three species of iris flowers (Setosa, Versicolor, and Virginica), with four

features: sepal length, sepal width, petal length, and petal width.

Objective: To classify the species of iris flowers based on their features using the Naive Bayes algorithm.

🔍 Key Steps:

Data Preparation: Loaded the dataset and explored its structure.

Data Partitioning: Used caret's createDataPartition to split the data into training (70%) and testing (30%) sets.

Model Training: Trained the Naive Bayes model using the e1071 package.

Model Evaluation: Evaluated the model's performance using confusion matrix,

Visualization: Created a confusion matrix heatmap and visualized the model performance

📈 Results:

Achieved a model accuracy of 91%