Shikta Sweekrita

• Objective - The objective of this project is to use data science techniques to develop a model that can accurately classify whether an individual has diabetes or not. By analyzing various independent variables related to the individual's health and lifestyle, we aim to create a reliable model that can identify the presence of diabetes in individuals. The ultimate goal of this project is to contribute to the development of effective tools for diabetes diagnosis and prevention.
• Methodology - The methodology for this project involves using exploratory data analysis techniques to analyze the relevant datasets and gain insights into the relationship between the independent variables and the presence of diabetes. We performed various data visualization techniques to identify correlations, outliers, and patterns in the data, which helped us to make informed decisions during the model selection process. Machine learning models such as Logistic Regression and Random Forest were applied to build a classification model that can predict whether an individual has diabetes or not. To evaluate the performance of the model, various metrics such as accuracy, precision, recall, and F1 score were used.
• Results - The results of our project demonstrate the potential of data science techniques in analyzing complex datasets. Our model achieved high accuracy and performed well in identifying the presence of diabetes in individuals. This project highlights the importance of machine learning and data science techniques in the field of healthcare, particularly in developing effective tools for diabetes diagnosis and prevention.
• Technical skills - Python, Pandas, Numpy, Matplotlib, Seaborn, scikit-learn, Microsoft Power BI, Power Point.

• Objective - The objective of this project is to use the independent variables to predict the species of flowers in the dataset, which contains samples from three different species. By analyzing the special features of each flower and utilizing machine learning techniques, the goal is to create an accurate and reliable model for predicting the species of flowers based on the provided dataset.
• Methodology - In this project, we applied data preprocessing techniques to clean and transform the dataset, including handling missing data, scaling the features, and encoding categorical variables. We then used a supervised machine learning model, specifically the Decision Tree algorithm, to classify the flowers based on their special features. To evaluate the performance of the model, we used several evaluation metrics such as accuracy, precision, recall, F1 score, and confusion matrix. We also conducted cross-validation. Overall, our methodology involved data preprocessing, machine learning model implementation, evaluation metrics, cross-validation, and feature selection, all of which were crucial steps in building an accurate and reliable model for predicting the species of flowers based on the given dataset.
• Conclusion - The results of our project demonstrate the effectiveness of the Decision Tree algorithm in predicting the species of flowers based on their special features. The model achieved high accuracy and performed well in classifying the samples from the three different species. Our project also shows the importance of proper data preprocessing and evaluation metrics, in building an accurate and reliable machine learning model.

Objective - The objective of this project is to develop a data science model for reducing the loss caused by credit card fraud. By analyzing transaction data and identifying patterns of fraudulent activities, we aim to create a reliable system that can detect and prevent fraudulent transactions.

Methodology - The methodology for this project involves pre-processing the transaction data and applying logistic regression to develop a model that can identify fraudulent transactions. The methodology included cleaning and transforming the data, which involved removing outliers and missing values. The model was trained on a labeled dataset that contains both fraudulent and non-fraudulent transactions. Regularization techniques were used to improve the model's generalization ability. To evaluate the performance of the model, various evaluation metrics such as accuracy, precision, recall, and F1 score were used.

Conclusion - Through our methodology, we were able to identify and analyze patterns in the transaction data that are associated with fraudulent activities, which can be used to further refine the model and improve its performance. Our project demonstrated the power of data analytics in detecting and preventing fraudulent transactions.