Add files via upload

2_Email.ipynb

@@ -0,0 +1,121 @@
+{
+  "metadata": {
+    "kernelspec": {
+      "name": "python",
+      "display_name": "Python (Pyodide)",
+      "language": "python"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "python",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8"
+    }
+  },
+  "nbformat_minor": 5,
+  "nbformat": 4,
+  "cells": [
+    {
+      "id": "1787aa40-6173-48cb-ac24-169a13a92b25",
+      "cell_type": "code",
+      "source": "import pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import accuracy_score, confusion_matrix, classification_report",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": 1
+    },
+    {
+      "id": "e529d131-cb7a-4408-8624-96b481da9f94",
+      "cell_type": "code",
+      "source": "data = pd.read_csv(\"emails.csv\")\nprint(data.head())",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": "  Email No.  the  to  ect  and  for  of    a  you  hou  ...  connevey  jay  \\\n0   Email 1    0   0    1    0    0   0    2    0    0  ...         0    0   \n1   Email 2    8  13   24    6    6   2  102    1   27  ...         0    0   \n2   Email 3    0   0    1    0    0   0    8    0    0  ...         0    0   \n3   Email 4    0   5   22    0    5   1   51    2   10  ...         0    0   \n4   Email 5    7   6   17    1    5   2   57    0    9  ...         0    0   \n\n   valued  lay  infrastructure  military  allowing  ff  dry  Prediction  \n0       0    0               0         0         0   0    0           0  \n1       0    0               0         0         0   1    0           0  \n2       0    0               0         0         0   0    0           0  \n3       0    0               0         0         0   0    0           0  \n4       0    0               0         0         0   1    0           0  \n\n[5 rows x 3002 columns]\n"
+        }
+      ],
+      "execution_count": 2
+    },
+    {
+      "id": "adcce56e-4742-4a1a-8ed6-82df3e5ad9c4",
+      "cell_type": "code",
+      "source": "X = data.drop(columns=['Email No.', 'Prediction'], errors='ignore')  # features\ny = data['Prediction'] ",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "id": "963f4ccd-9790-4d1f-8458-995bdbfc84eb",
+      "cell_type": "code",
+      "source": "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "id": "481d8d23-ce4d-4419-a820-779ad333be2a",
+      "cell_type": "code",
+      "source": "scaler = StandardScaler()\nX_train = scaler.fit_transform(X_train)\nX_test = scaler.transform(X_test)",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "id": "255b9746-202d-425b-9353-41d7bb18d99c",
+      "cell_type": "code",
+      "source": "knn = KNeighborsClassifier(n_neighbors=5)\nknn.fit(X_train, y_train)\ny_pred_knn = knn.predict(X_test)",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "id": "0c5e2c44-afc4-43d8-bdd7-e0a136d54413",
+      "cell_type": "code",
+      "source": "svm = SVC(kernel='linear', C=1)\nsvm.fit(X_train, y_train)\ny_pred_svm = svm.predict(X_test)",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "id": "77909086-350b-4442-97d6-e7bbef15648f",
+      "cell_type": "code",
+      "source": "print(\"===== KNN Model Evaluation =====\")\nprint(\"Accuracy:\", accuracy_score(y_test, y_pred_knn))\nprint(confusion_matrix(y_test, y_pred_knn))\nprint(classification_report(y_test, y_pred_knn))",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "id": "72a0433c-8085-4ea4-aa29-28ed856ab086",
+      "cell_type": "code",
+      "source": "print(\"\\n===== SVM Model Evaluation =====\")\nprint(\"Accuracy:\", accuracy_score(y_test, y_pred_svm))\nprint(confusion_matrix(y_test, y_pred_svm))\nprint(classification_report(y_test, y_pred_svm))",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
+}

3_bank_customer.ipynb

@@ -0,0 +1,95 @@
+{
+  "metadata": {
+    "kernelspec": {
+      "name": "python",
+      "display_name": "Python (Pyodide)",
+      "language": "python"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "python",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8"
+    }
+  },
+  "nbformat_minor": 5,
+  "nbformat": 4,
+  "cells": [
+    {
+      "id": "d4cec5b7-5725-44d3-bfb7-04278fdf9bb4",
+      "cell_type": "code",
+      "source": "import pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler, LabelEncoder\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.metrics import accuracy_score, confusion_matrix",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": 2
+    },
+    {
+      "id": "0e3d0d27-300b-4152-96e0-70ff1fbab83a",
+      "cell_type": "code",
+      "source": "data = pd.read_csv(\"Churn_Modelling.csv\")",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": 3
+    },
+    {
+      "id": "06fd9a81-ed4d-4796-bc38-e0c68fe1dc3e",
+      "cell_type": "code",
+      "source": "X = data.iloc[:, 3:13]   # Features from CreditScore to EstimatedSalary\ny = data.iloc[:, 13] ",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": 4
+    },
+    {
+      "id": "90c9c5aa-0b8a-424b-a625-ff4fc2d73380",
+      "cell_type": "code",
+      "source": "le = LabelEncoder()\nX[\"Gender\"] = le.fit_transform(X[\"Gender\"])\nX = pd.get_dummies(X, columns=[\"Geography\"], drop_first=True)",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": 5
+    },
+    {
+      "id": "4b0ecfe6-7245-4866-a842-e422c5658928",
+      "cell_type": "code",
+      "source": "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": 6
+    },
+    {
+      "id": "55cd9306-83f0-4ea6-8399-278444f3e839",
+      "cell_type": "code",
+      "source": "scaler = StandardScaler()\nX_train = scaler.fit_transform(X_train)\nX_test = scaler.transform(X_test)",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": 7
+    },
+    {
+      "id": "60cdf8fb-c656-4a24-a120-de0c4c9abf94",
+      "cell_type": "code",
+      "source": "",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
+}

4_GDA.py

@@ -0,0 +1,19 @@
+def f(x):
+    return (x + 3)**2
+
+def df(x):
+    return 2 * (x + 3)
+
+# Step 2: Initialize parameters
+x = 2                # starting point
+learning_rate = 0.1  # step size
+epochs = 30          # number of iterations
+
+# Step 3: Gradient Descent loop
+for i in range(epochs):
+    grad = df(x)                    # compute gradient
+    x = x - learning_rate * grad    # update x
+    print(f"Iteration {i+1}: x = {x:.4f}, f(x) = {f(x):.4f}")
+
+print("\nLocal minima occurs at x =", round(x, 4))
+print("Minimum value of function =", round(f(x), 4))

6_Kmeans_salesd.ipynb

@@ -0,0 +1,8 @@
+{
+  "metadata": {
+    "orig_nbformat": 4
+  },
+  "nbformat_minor": 5,
+  "nbformat": 4,
+  "cells": []
+}