{"id":28412738,"url":"https://github.com/whereishussain/data-science","last_synced_at":"2025-06-24T13:30:57.024Z","repository":{"id":287572133,"uuid":"965157476","full_name":"WhereisHussain/Data-Science","owner":"WhereisHussain","description":"Projects related Data Visualisation, Cleaning, Preprocessing, Machine Learning, Deep Learning, ANN and CNN Projects and Model Training and Model Evaluation","archived":false,"fork":false,"pushed_at":"2025-05-09T22:16:44.000Z","size":462,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-03T12:15:52.302Z","etag":null,"topics":["data-cleaning-and-preprocessing","data-science","data-visualisation","machine-learning","machine-learning-models","model-training-and-evaluation","neural-networks"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🧠 Data Science Projects Repository\n\nThis repository contains a collection of practical data science projects covering the complete machine learning workflow, including:\n\n- 🧹 Data Cleaning\n- 🧪 Data Preprocessing\n- 📊 Data Visualization\n- 🏗️ Model Building \u0026 Compilation\n- 🏃 Model Training \u0026 Evaluation\n\nThese projects aim to help learners and practitioners understand each phase of working with data and machine learning models.\n\n---\n\n## 📁 Project Structure\n\n### 1. 🧼 Data Cleaning Project\n**Objective:**  \nClean and standardize a raw dataset with missing values, duplicates, incorrect data types, and inconsistent formatting.\n\n**Techniques Used:**\n- Handling missing data (mean, median, drop)\n- Removing duplicates\n- Converting data types\n- String formatting and trimming\n- Date and time conversion\n\n**Tools:** `pandas`, `numpy`\n\n📄 File: `data_cleaning.py`\n\n---\n\n### 2. ⚙️ Data Preprocessing Project\n**Objective:**  \nPrepare clean data for machine learning algorithms by transforming features and labels.\n\n**Techniques Used:**\n- Feature scaling (StandardScaler, MinMaxScaler)\n- Encoding categorical variables (OneHotEncoder, LabelEncoder)\n- Train-test split\n- Data balancing (optional: SMOTE)\n\n**Tools:** `pandas`, `scikit-learn`, `numpy`\n\n\n---\n\n### 3. 📈 Data Visualization Project\n**Objective:**  \nUnderstand the dataset using visual exploration techniques and identify patterns or anomalies.\n\n**Techniques Used:**\n- Histograms, box plots, scatter plots\n- Correlation heatmaps\n- Pair plots\n- Class distribution graphs\n\n**Tools:** `matplotlib`, `seaborn`, `pandas`\n\n\n---\n\n### 4. 🧠 Model Compilation \u0026 Training Project\n**Objective:**  \nBuild a machine learning or deep learning model, compile it with appropriate configurations, and train it on prepared data.\n\n**Steps Covered:**\n- Defining a model (ML or DL)\n- Choosing loss function, optimizer, metrics\n- Model training with validation\n- Accuracy and loss plots\n\n**Tools:** `scikit-learn`, `keras` / `tensorflow`, `matplotlib`\n\n\n---\n\n### 5. 🔍 Evaluation \u0026 Testing\n**Objective:**  \nEvaluate model performance using appropriate metrics and visualize the results.\n\n**Evaluation Metrics:**\n- Accuracy, precision, recall, F1-score\n- Confusion matrix\n- ROC-AUC (for classification)\n\n\n\n---\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwhereishussain%2Fdata-science","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwhereishussain%2Fdata-science","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwhereishussain%2Fdata-science/lists"}