Skin Cancer Detection using Deep neural network algorithms.

The project uses advanced convolutional neural networks (DenseNet, EfficientNet, ResNet) with data preprocessing, augmentation, and Keras Tuner for hyperparameter optimization to classify skin cancer images.

Uber Dataset Exploratory Data Analysis

Conduct exploratory data analysis on the Uber trip dataset to understand trip distribution and categorize trips into business and personal.

Data Analysis and Hypothesis Testing

Performed data analysis and hypothesis testing on various datasets. Conducted statistical tests such as t-tests and chi-square tests. Analyzed McDonald's nutritional dataset to evaluate hypotheses on nutritional values. Applied A/B testing to assess the effectiveness of different strategies.

McDonald's Dataset EDA

Conducted exploratory data analysis on McDonald's menu dataset. Analyzed nutritional content of menu items to identify high-calorie foods. Cleaned and preprocessed data to ensure accuracy. Identified and reported on items with the highest and lowest nutritional values.

Advanced Statistical Analysis

Conducted multivariate statistical analysis on complex datasets. Applied principal component analysis (PCA) to reduce dimensionality and uncover hidden patterns. Utilized cluster analysis to segment data into meaningful groups. Performed hypothesis testing to validate statistical significance.

Clustering and Classification Analysis

Implemented clustering algorithms (K-Means, Hierarchical) to segment data into meaningful groups. Built classification models (Logistic Regression, SVM) to predict categorical outcomes. Evaluated model performance using metrics such as accuracy, precision, recall, and F1 score. Visualized clustering

ML Model Building, Hyperparameter Tuning, and Text Analytics

Developed machine learning models for classification and regression tasks. Performed hyperparameter tuning using GridSearchCV and RandomizedSearchCV to optimize model performance. Conducted text analytics using NLP techniques such as TF-IDF and sentiment analysis. Evaluated model performance with me

Predictive Modelling

Built predictive models to forecast future trends and behaviors. Conducted data preprocessing and feature engineering to enhance model performance. Utilized machine learning algorithms such as Linear Regression, Decision Trees, and Random Forest. Evaluated model accuracy using metrics like RMSE, MAE

Time Series Analysis on ROSE Dataset

Conducted time series analysis on the ROSE dataset. Performed data preprocessing and transformation for time series modeling. Applied time series decomposition to analyze trend, seasonality, and residual components. Implemented forecasting models such as ARIMA and Exponential Smoothing.

Time Series Analysis on Sparkling Dataset

Conducted time series analysis on the Sparkling dataset. Performed data cleaning and preprocessing for time series data. Applied time series decomposition to identify trend, seasonality, and residual components. Developed forecasting models such as ARIMA and Holt-Winters Exponential Smoothing.

KNIME MRA Project Analysis

Performed data preprocessing and cleaning using KNIME. Implemented data transformation and aggregation workflows. Applied machine learning algorithms for predictive analysis. Conducted data visualization to present insights. Utilized various KNIME nodes for data manipulation and model evaluation.

MRA Analysis Report- CAFE

The Tableau Public report titled "MRA Cafe Final Report" provides an in-depth analysis of café data, including sales performance, customer demographics, and product preferences. It includes interactive dashboards that visualize trends in revenue, popular menu items, and customer visit patterns.