Mohamed Shaad's repositories
Customer-Segmentation-KMeansClustering
This project involves segmenting customers using k-means clustering in Jupyter Notebook. Customer segmentation is a powerful technique used in marketing and business analytics to divide customers into distinct groups based on their behaviors, preferences, or demographics.
Breast-Cancer-Detection-NaiveBayesClassifer
This project involves detecting breast cancer using the Naive Bayes classifier in Jupyter Notebook. Breast cancer detection is a crucial task in healthcare, as it aids in the early diagnosis and treatment of the disease. Through this project, we aim to explore and understand how the Naive Bayes classifier can be used for breast cancer detection.
Height-Prediction-PolynomialRegression
This project involves predicting height based on age using polynomial regression in Jupyter Notebook. Polynomial regression is a variation of linear regression that models the relationship between the independent variable (age) and the dependent variable (height) as an nth-degree polynomial.
Student-Performance-Analysis
This project involves the analysis of student performance using Seaborn plots in Jupyter Notebook. The dataset contains information about students' demographics, study habits, and performance in various subjects. Through this analysis, we aim to gain insights into the factors that influence student performance.
Heart-Disease-Prediction-KNN
This project focuses on predicting heart disease using the K-Nearest Neighbors (KNN) classification algorithm implemented in a Jupyter Notebook. It aims to provide a tool that can assist in early detection and diagnosis of heart disease based on given input features.
Matplotlib-Exercises
This project provides a collection of Jupyter Notebook exercises for practicing Matplotlib plots, including bar plots, histograms, pie charts, and scatter plots. Matplotlib is a powerful data visualization library in Python that allows for creating a wide range of plots and visualizations.
NumPy-Exercises
This project provides a collection of Jupyter Notebook exercises for practicing NumPy, a fundamental library for numerical computing in Python. NumPy provides powerful data structures and functions for handling large, multi-dimensional arrays and matrices. Through this project, we aim to enhance our skills in NumPy.
Spotify-Dataset-EDA
This project is an Exploratory Data Analysis (EDA) on the Spotify dataset. The dataset contains information about various songs, including their features such as danceability, energy, loudness, and more. Through this analysis, we aim to gain insights into the characteristics of the songs and explore any patterns or trends.
Diabetes-Progression-Prediction-RidgeRegression
This project involves the prediction of diabetes progression using Ridge Regression in Jupyter Notebook. The dataset contains features such as glucose level, blood pressure, body mass index, and more. Through this analysis, we aim to build a regression model that accurately predicts the progression of diabetes based on the given input features.
Energy-Output-Prediction-MultipleLinearRegressor
This project involves the prediction of energy output in a Combined Cycle Power Plant (CCPP) using Multiple Linear Regression in Jupyter Notebook. The dataset contains features such as temperature, pressure, humidity, and exhaust vacuum, which are used to predict the net hourly electrical energy output.
Fake-News-Detection-DecisionTreeClassifier
This project involves detecting fake news using a decision tree classifier in Jupyter Notebook. Fake news detection is an important task in the field of natural language processing and machine learning, as it helps identify and filter out misleading or false information.
Feature-Selection-Techniques
This project involves the implementation of different feature selection techniques in Jupyter Notebook for practice. Feature selection is an important step in machine learning that aims to select the most relevant features from a given dataset. Through this project, we aim to explore and understand various feature selection techniques.
Kozhikode-Pollution-Analysis
This project involves the analysis of pollution data in Kozhikode city, where the data is acquired using an API and visualized using Matplotlib in Jupyter Notebook. The project aims to gain insights into the pollution levels during a week and visualize the trends and patterns.
Salary-Prediction-SupportVectorRegressor
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.
Scikit-learn-Exercises
This project provides a collection of Jupyter Notebook exercises for practicing scikit-learn, a popular machine learning library in Python. Scikit-learn provides a wide range of machine learning algorithms, tools for data preprocessing, model evaluation, and more. Through this project, we aim to enhance our skills in Scikit-learn.
Statistics-Python
This project provides a collection of Jupyter Notebook exercises for practicing statistics concepts using Python. Statistics is a fundamental field in data analysis and plays a crucial role in understanding and interpreting data. Through this project, we aim to enhance our statistical skills by implementing various concepts using Python.
Boston-House-Price-Prediction-LassoRegression
This project involves the prediction of house prices in Boston using Lasso Regression in Jupyter Notebook. The dataset contains features such as average number of rooms per dwelling, crime rate, and more. Through this analysis, we aim to build a regression model that accurately predicts house prices based on the given input features.
BusinessCard-DataExtraction-OCR-NER
This project aims to extract structured data from business cards using a combination of OpenCV, PyTesseract, and spaCy.
Car-Price-Prediction-LinearRegression
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.
Django-Login-Page
This project implements a user login and authentication system using Django, allowing users to sign in, sign out, and access protected views.
Feature-Engineering-Techniques
This project involves the implementation of different feature engineering techniques in Jupyter Notebook for practice. Feature engineering is a crucial step in machine learning that involves transforming raw data into meaningful features to improve model performance. Through this project, we aim to practice various feature engineering techniques.
House-Price-Prediction-DecisionTreeRegressor
This project involves the prediction of house prices in Bengaluru city using Decision Tree Regression in Jupyter Notebook. Through this analysis, we aim to build a regression model that accurately predicts house prices based on the given input features.
Iris-Species-Detection-KNN
This project involves detecting iris species using the k-nearest neighbors (KNN) algorithm in Jupyter Notebook. The iris species detection task is a classic problem in machine learning, where the goal is to classify iris flowers into different species based on their measurements.
Pandas-Exercises
This project provides a collection of Jupyter Notebook exercises for practicing pandas, a powerful data manipulation and analysis library in Python. pandas offers a wide range of functions and methods for handling and analyzing structured data. Through this project, we aim to enhance our skills in pandas.
Password-Strength-Checker-RandomForestClassifier
This project is a password strength checker that utilizes a Random Forest Classifier to determine the strength of a given password. The Random Forest Classifier is trained on a dataset of passwords labeled with their corresponding strength levels.
Seaborn-Exercises
This project provides a collection of Seaborn exercise plots implemented in Jupyter Notebook for practice. Seaborn is a powerful data visualization library in Python that offers a variety of statistical plots and visualization techniques. Through this project, we aim to enhance our skills in data visualization using Seaborn.
Wine-Quality-Prediction-LogisticRegression
This project involves predicting wine quality using logistic regression in Jupyter Notebook. Wine quality prediction is an important task in the field of wine production and quality control, as it helps assess the overall quality of wines based on various chemical properties.
Zomato-Dataset-Analysis
This project involves the analysis of the Zomato dataset for restaurants in Bengaluru city. The dataset provides information about various restaurants, including their ratings, cuisines, costs, and more. Through this analysis, we aim to gain insights into the restaurant landscape in Bengaluru and explore factors that influence ratings.
Data-Structures-Algorithms
This repository contains a collection of classic and essential data structures and algorithms implemented in Python.
Currency-Exchange-Rate-Forecasting
This repository contains code and data for analyzing the USD - INR conversion rate over the years. The analysis includes data visualization, growth analysis, seasonal decomposition, and time series forecasting using SARIMA.