Dependencies: -pandas -numpy -scikit-learn -matplotlib -seaborn -jupyter
Task 1: TITANIC SURVIVAL PREDICTION
Overview: This project aims to predict whether a passenger on the Titanic survived or not based on various features such as age, gender, ticket class, fare, and cabin. The dataset used for this project is a classic and readily available dataset, providing information about individual passengers on the Titanic.
Task 2: MOVIE RATING PREDICTION WITH PYTHON
Overview: This Movie Rating Prediction project aims to build a model that predicts the rating of a movie based on various features such as genre, director, and actors. The goal is to analyze historical movie data, perform data analysis, preprocessing, feature engineering, and use regression techniques to develop a model that accurately estimates movie ratings given by users or critics.
Task 3:IRIS FLOWER CLASSIFICATION
Overview: The Iris Flower Classification project aims to build a machine learning model that accurately classifies Iris flowers into three species: setosa, versicolor, and virginica. The classification is based on measurements of the flowers' sepal length, sepal width, petal length, and petal width. The Iris dataset is a popular choice for introductory classification tasks in machine learning.
Task 4: SALES PREDICTION USING PYTHON
Overview: The Sales Prediction Using Python project aims to leverage machine learning techniques to forecast the amount of a product that customers will purchase. The prediction takes into account various factors such as advertising expenditure, target audience segmentation, and advertising platform selection. In businesses offering products or services, data scientists play a crucial role in predicting future sales, enabling informed decisions regarding advertising costs and optimization of advertising strategies.
Task 5: CREDIT CARD FRAUD DETECTION
Overview: The Credit Card Fraud Detection project focuses on building a machine learning model to identify fraudulent credit card transactions. The process involves preprocessing and normalizing transaction data, addressing class imbalance issues, and splitting the dataset into training and testing sets. A classification algorithm, such as logistic regression or random forests, is trained to classify transactions as either fraudulent or genuine. Evaluation metrics such as precision, recall, and F1-score are used to assess the model's performance. Additionally, techniques like oversampling or undersampling may be employed to improve results.