Merge branch 'main' of https://github.com/Nethmi11/ML-Nexus

.github/workflows/auto_label.yml

@@ -17,14 +17,13 @@ jobs:
       with:
         github_token: ${{ secrets.GITHUB_TOKEN }} 
         labels: |
-          gssoc-ext
-          hacktoberfest-accepted
+          ml-nexus
 
     - name: Add labels to new pull requests
       if: github.event_name == 'pull_request_target'
       uses: actions-ecosystem/action-add-labels@v1
       with:
         github_token: ${{ secrets.GITHUB_TOKEN }}
         labels: |
-          gssoc-ext
-          hacktoberfest-accepted
+          ml-nexus
+          

.github/workflows/autocomment-issue.yml

@@ -17,7 +17,7 @@ jobs:
         run: |
           COMMENT=$(cat <<EOF
           {
-            "body": "Thanks for creating the issue in ML-Nexus!🎉\nBefore you start working on your PR, please make sure to:\n - ⭐ Star the repository if you haven't already.\n - Pull the latest changes to avoid any merge conflicts.\n - Attach before & after screenshots in your PR for clarity.\n- Include the issue number in your PR description for better tracking.\n  Don't forget to follow @UppuluriKalyani – Project Admin – for more updates!\nTag @Neilblaze,@SaiNivedh26 for assigning the issue to you.\nHappy open-source contributing!☺️"
+            "body": "Thanks for creating the issue in ML-Nexus!🎉\nBefore you start working on your PR,\nPull the latest changes to avoid any merge conflicts.\n - Attach before & after screenshots in your PR for clarity.\n- Include the issue number in your PR description for better tracking.\nHappy open-source contributing!☺️"
           }
           EOF
           )

.github/workflows/autocomment-pr-raise.yml

@@ -17,7 +17,7 @@ jobs:
       run: |
         COMMENT=$(cat <<EOF
         {
-          "body": "👋 Thank you for opening this pull request! We appreciate your contribution to improving this project. Your PR is under review, and we'll get back to you shortly. \nDon't forget to mention the issue you solved!.\n\nTo help move the process along, please tag @UppuluriKalyani, @Neilblaze, and @SaiNivedh26 for a faster review!"
+          "body": "👋 Thank you for opening this pull request! We appreciate your contribution to improving this project. Your PR is under review, and we'll get back to you shortly. \nDon't forget to mention the issue you solved!"
         }
         EOF
         )
@@ -37,7 +37,7 @@ jobs:
       run: |
         REVIEWERS=$(cat <<EOF
         {
-          "reviewers": ["UppuluriKalyani","Neilblaze","SaiNivedh26"]
+          "reviewers": ["UppuluriKalyani"]
         }
         EOF
         )

Anomaly Detection Project/Anomaly_Detection_Project.ipynb

@@ -0,0 +1,43 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Import necessary libraries\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.ensemble import IsolationForest\n",
+    "from sklearn.cluster import KMeans\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.metrics import confusion_matrix, classification_report\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "# Set plot style\n",
+    "sns.set(style=\"whitegrid\")\n",
+    "\n",
+    "# Load Dataset\n",
+    "# Replace 'data.csv' with your dataset file\n",
+    "data = pd.read_csv('data.csv')\n",
+    "\n",
+    "# Display the first few rows\n",
+    "data.head()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Anomaly Detection Project/anomalies_report.py

@@ -0,0 +1,15 @@
+# anomalies_report.py
+
+import pandas as pd
+
+# Load preprocessed data
+data = pd.read_csv('preprocessed_data.csv')
+
+# Identify anomalies based on both clustering and isolation forest
+anomalies_kmeans = data[data['Cluster'] == 1]  # From KMeans
+anomalies_isoforest = data[data['Anomaly'] == 1]  # From Isolation Forest
+
+# Combine and save identified anomalies from both methods
+anomalies_report = pd.concat([anomalies_kmeans, anomalies_isoforest]).drop_duplicates()
+anomalies_report.to_csv('anomalies_report.csv', index=False)
+print("Anomalies report saved as 'anomalies_report.csv'.")

Anomaly Detection Project/data_preprocessing.py

@@ -0,0 +1,19 @@
+# data_preprocessing.py
+
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+
+# Load dataset
+data = pd.read_csv('raw_data.csv')  # Replace with the actual path to your dataset
+
+# Check for missing values and drop rows with missing values
+data = data.dropna()
+
+# Standardize data
+scaler = StandardScaler()
+data_scaled = scaler.fit_transform(data)
+data_scaled = pd.DataFrame(data_scaled, columns=data.columns)
+
+# Save preprocessed data to CSV
+data_scaled.to_csv('preprocessed_data.csv', index=False)
+print("Preprocessed data saved to 'preprocessed_data.csv'.")

Anomaly Detection Project/eda_visualizations.py

@@ -0,0 +1,21 @@
+# eda_visualizations.py
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Load preprocessed data
+data = pd.read_csv('preprocessed_data.csv')
+
+# Set plot style
+sns.set(style="whitegrid")
+
+# Plot feature distributions
+fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(15, 10))
+axes = axes.flatten()
+for i, col in enumerate(data.columns):
+    sns.histplot(data[col], kde=True, ax=axes[i])
+    axes[i].set_title(f"Distribution of {col}")
+plt.tight_layout()
+plt.savefig('eda_feature_distributions.png')
+plt.show()

Anomaly Detection Project/isolation_forest_anomaly_detection.py

@@ -0,0 +1,38 @@
+# isolation_forest_anomaly_detection.py
+
+import pandas as pd
+from sklearn.ensemble import IsolationForest
+import joblib
+from sklearn.decomposition import PCA
+import matplotlib.pyplot as plt
+
+# Load preprocessed data
+data = pd.read_csv('preprocessed_data.csv')
+
+# Set Isolation Forest parameters
+contamination_rate = 0.05  # Adjust this value based on your dataset and anomaly expectations
+max_samples_value = "auto"  # Can be an integer or "auto" (256 or total sample size, whichever is smaller)
+
+# Isolation Forest with custom parameters
+iso_forest = IsolationForest(contamination=contamination_rate, max_samples=max_samples_value, random_state=42)
+data['Anomaly'] = iso_forest.fit_predict(data)
+
+# Convert labels for anomalies
+data['Anomaly'] = data['Anomaly'].apply(lambda x: 1 if x == -1 else 0)
+
+# Save the Isolation Forest model
+joblib.dump(iso_forest, 'isolation_forest_model.pkl')
+print("Isolation Forest model saved as 'isolation_forest_model.pkl'.")
+
+# Visualize anomalies with PCA
+pca = PCA(n_components=2)
+pca_data = pca.fit_transform(data.drop(columns=['Anomaly']))
+
+plt.figure(figsize=(10, 6))
+plt.scatter(pca_data[:, 0], pca_data[:, 1], c=data['Anomaly'], cmap='coolwarm', marker='o', alpha=0.6)
+plt.title("Isolation Forest Anomaly Detection with PCA")
+plt.xlabel("PCA Component 1")
+plt.ylabel("PCA Component 2")
+plt.colorbar(label="Anomaly")
+plt.savefig('isolation_forest_anomalies_pca.png')
+plt.show()

Anomaly Detection Project/kmeans_anomaly_detection.py

@@ -0,0 +1,35 @@
+# kmeans_anomaly_detection.py
+
+import pandas as pd
+from sklearn.cluster import KMeans
+import joblib
+from sklearn.decomposition import PCA
+import matplotlib.pyplot as plt
+
+# Load preprocessed data
+data = pd.read_csv('preprocessed_data.csv')
+
+# KMeans Clustering
+kmeans = KMeans(n_clusters=2, random_state=42)
+kmeans.fit(data)
+data['Cluster'] = kmeans.labels_
+
+# Save the KMeans model
+joblib.dump(kmeans, 'kmeans_model.pkl')
+print("KMeans model saved as 'kmeans_model.pkl'.")
+
+# Identify anomalies based on cluster assignment
+anomalies_kmeans = data[data['Cluster'] == 1]
+
+# Visualize clusters with PCA
+pca = PCA(n_components=2)
+pca_data = pca.fit_transform(data.drop(columns=['Cluster']))
+
+plt.figure(figsize=(10, 6))
+plt.scatter(pca_data[:, 0], pca_data[:, 1], c=data['Cluster'], cmap='viridis', marker='o', alpha=0.6)
+plt.title("KMeans Clustering with PCA")
+plt.xlabel("PCA Component 1")
+plt.ylabel("PCA Component 2")
+plt.colorbar(label="Cluster")
+plt.savefig('kmeans_clusters_pca.png')
+plt.show()

Anomaly Detection Project/model_evaluation.py

@@ -0,0 +1,23 @@
+# model_evaluation.py
+
+import pandas as pd
+from sklearn.metrics import confusion_matrix, classification_report
+
+# Load data with true labels (replace 'TrueLabels' with the actual column if available)
+data = pd.read_csv('preprocessed_data.csv')
+# Assuming `TrueLabels` column exists in the original data
+
+# Evaluate Isolation Forest Model
+# Assuming 'TrueLabels' is in the original data and represents ground truth for anomalies
+# Uncomment and use if true labels are available
+
+# true_labels = data['TrueLabels']
+# print("Confusion Matrix (Isolation Forest):\n", confusion_matrix(true_labels, data['Anomaly']))
+# print("Classification Report (Isolation Forest):\n", classification_report(true_labels, data['Anomaly']))
+
+# Save the evaluation report to file
+# with open("evaluation_report.txt", "w") as f:
+#     f.write("Confusion Matrix (Isolation Forest):\n")
+#     f.write(str(confusion_matrix(true_labels, data['Anomaly'])) + "\n\n")
+#     f.write("Classification Report (Isolation Forest):\n")
+#     f.write(classification_report(true_labels, data['Anomaly']))