Scikit_Learn_Machine_Learning_in_Python_.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyO5mXKywMbgCGo4CsfMj/pI",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lovnishverma/Python-Getting-Started/blob/main/Scikit_Learn_Machine_Learning_in_Python_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Scikit-Learn: Machine Learning in Python (Beginner Guide)**  \n",
        "\n",
        "Scikit-learn is one of the most popular machine learning libraries in Python. It provides simple and efficient tools for predictive data analysis.\n"
      ],
      "metadata": {
        "id": "CZp50RYW5DCi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **1. Installation and Setup**\n",
        "First, install Scikit-learn and its dependencies using:"
      ],
      "metadata": {
        "id": "8AzavVKZ5JAK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "```bash\n",
        "pip install scikit-learn numpy pandas matplotlib\n",
        "```"
      ],
      "metadata": {
        "id": "NBOqiErF5Lnv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2. Basics of Scikit-learn**\n",
        "Scikit-learn provides:\n",
        "- **Supervised Learning** (Regression & Classification)\n",
        "- **Unsupervised Learning** (Clustering & Dimensionality Reduction)\n",
        "- **Model Selection & Evaluation**\n",
        "- **Feature Engineering & Preprocessing**"
      ],
      "metadata": {
        "id": "GervC5MX5Osg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Importing Required Libraries**"
      ],
      "metadata": {
        "id": "IgOnVv-x63BR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix"
      ],
      "metadata": {
        "id": "R_xe_j2U5Tch"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **3. Supervised Learning (Regression & Classification)**"
      ],
      "metadata": {
        "id": "8-eC9AGa5WHP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3.1 Linear Regression (Predicting Continuous Values)**"
      ],
      "metadata": {
        "id": "DyhdIpVh5ZTA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "# Sample dataset\n",
        "X = np.array([[1], [2], [3], [4], [5]])  # Features\n",
        "y = np.array([2, 4, 6, 8, 10])  # Target variable\n",
        "\n",
        "# Train-test split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Model training\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Prediction\n",
        "y_pred = model.predict(X_test)\n",
        "print(\"Predicted Values:\", y_pred)\n",
        "\n",
        "# Model evaluation\n",
        "print(\"Model Coefficient:\", model.coef_)\n",
        "print(\"Model Intercept:\", model.intercept_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "InPvtMiW5bg3",
        "outputId": "fcfcfa7e-7b8c-486c-8254-7605e7ef7a24"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Values: [4.]\n",
            "Model Coefficient: [2.]\n",
            "Model Intercept: 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3.2 Logistic Regression (Binary Classification)**"
      ],
      "metadata": {
        "id": "oywfG__x5e9R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.datasets import load_iris\n",
        "\n",
        "# Load dataset\n",
        "iris = load_iris()\n",
        "X, y = iris.data[:, :2], iris.target  # Using only 2 features for visualization\n",
        "\n",
        "# Train-test split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Model training\n",
        "log_model = LogisticRegression()\n",
        "log_model.fit(X_train, y_train)\n",
        "\n",
        "# Prediction & Evaluation\n",
        "y_pred = log_model.predict(X_test)\n",
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0QyVmZbq5hB_",
        "outputId": "19d6f332-e798-4db7-8478-1f51a599db54"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **4. Unsupervised Learning (Clustering & Dimensionality Reduction)**\n",
        "\n",
        "### **4.1 K-Means Clustering**"
      ],
      "metadata": {
        "id": "90M1EymL5kXv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "\n",
        "# Generate sample data\n",
        "X = np.array([[1, 2], [1, 4], [1, 0], [10, 2], [10, 4], [10, 0]])\n",
        "\n",
        "# Create and fit model\n",
        "kmeans = KMeans(n_clusters=2, random_state=42)\n",
        "kmeans.fit(X)\n",
        "\n",
        "# Predictions\n",
        "print(\"Cluster Labels:\", kmeans.labels_)\n",
        "print(\"Cluster Centers:\", kmeans.cluster_centers_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pNY91fBv5mcY",
        "outputId": "98d497e6-e2c8-4da5-dd25-cbd84b00787b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cluster Labels: [0 0 0 1 1 1]\n",
            "Cluster Centers: [[ 1.  2.]\n",
            " [10.  2.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **4.2 Principal Component Analysis (PCA) for Dimensionality Reduction**"
      ],
      "metadata": {
        "id": "JjIxZHcu5pdo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "# Load dataset\n",
        "X, y = iris.data, iris.target\n",
        "\n",
        "# Apply PCA\n",
        "pca = PCA(n_components=2)\n",
        "X_pca = pca.fit_transform(X)\n",
        "\n",
        "# Plot PCA result\n",
        "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y, cmap=\"viridis\")\n",
        "plt.xlabel(\"Principal Component 1\")\n",
        "plt.ylabel(\"Principal Component 2\")\n",
        "plt.title(\"PCA on Iris Dataset\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "ipygW19K5rLP",
        "outputId": "faf453e7-b88d-486c-98ac-2df083705994"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **5. Model Selection & Evaluation**\n",
        "### **5.1 Train-Test Split & Cross-Validation**"
      ],
      "metadata": {
        "id": "1lVFQkPg5tiX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# Cross-validation score for logistic regression\n",
        "scores = cross_val_score(log_model, X, y, cv=5)\n",
        "print(\"Cross-validation Scores:\", scores)\n",
        "print(\"Mean Score:\", scores.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TQ5g2mJj5vRv",
        "outputId": "88ebcc78-7470-4266-8b3d-71cb30c2b79e"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross-validation Scores: [0.96666667 1.         0.93333333 0.96666667 1.        ]\n",
            "Mean Score: 0.9733333333333334\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **5.2 Confusion Matrix**"
      ],
      "metadata": {
        "id": "qpbtqdhM5xwR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(\"Confusion Matrix:\\n\", cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NeifKlU55zSg",
        "outputId": "4e378a70-a9e3-429c-c765-058229ae14b5"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            " [[10  0  0]\n",
            " [ 0  7  2]\n",
            " [ 0  1 10]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **6. Feature Engineering & Preprocessing**\n",
        "### **6.1 Standardization & Normalization**"
      ],
      "metadata": {
        "id": "WCVzLG4l51lw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
        "\n",
        "# Standardization\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "# Normalization\n",
        "normalizer = MinMaxScaler()\n",
        "X_normalized = normalizer.fit_transform(X)"
      ],
      "metadata": {
        "id": "a6o50Idz53-v"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **7. Advanced Topics**\n",
        "### **7.1 Decision Trees & Random Forest**"
      ],
      "metadata": {
        "id": "cxyzJOGm57Bf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# Train Decision Tree\n",
        "dt_model = DecisionTreeClassifier()\n",
        "dt_model.fit(X_train, y_train)\n",
        "\n",
        "# Train Random Forest\n",
        "rf_model = RandomForestClassifier(n_estimators=100)\n",
        "rf_model.fit(X_train, y_train)\n",
        "\n",
        "# Prediction & Evaluation\n",
        "print(\"Decision Tree Accuracy:\", accuracy_score(y_test, dt_model.predict(X_test)))\n",
        "print(\"Random Forest Accuracy:\", accuracy_score(y_test, rf_model.predict(X_test)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "92omA__z59PX",
        "outputId": "65debf87-867e-4d65-f5b6-c260cab4fa40"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree Accuracy: 0.6333333333333333\n",
            "Random Forest Accuracy: 0.8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **7.2 Support Vector Machines (SVM)**"
      ],
      "metadata": {
        "id": "tiOn3Pch5_w3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "# Train SVM model\n",
        "svm_model = SVC(kernel='linear')\n",
        "svm_model.fit(X_train, y_train)\n",
        "\n",
        "# Prediction & Evaluation\n",
        "y_pred = svm_model.predict(X_test)\n",
        "print(\"SVM Accuracy:\", accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BPf9gTYi6BjH",
        "outputId": "7c61f142-1e40-4cc1-8941-5451d0a04547"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVM Accuracy: 0.9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **What’s Next?**\n",
        "- **Hyperparameter Tuning** (GridSearchCV, RandomizedSearchCV)\n",
        "- **Deep Learning with Scikit-learn**\n",
        "- **Time Series Forecasting**\n",
        "- **Building a Complete ML Pipeline**\n",
        "\n",
        "Cover topics like **model tuning (GridSearchCV, RandomizedSearchCV)** & **deployment of models** 😊"
      ],
      "metadata": {
        "id": "nxrln0qA6DxJ"
      }
    }
  ]
}