Pranav activity model

AI_models_pranav/dataset-sisfallpy

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+import os
+import pandas as pd
+import matplotlib.pyplot as plt
+
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.svm import SVC
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, ConfusionMatrixDisplay
+
+# ============================
+# 1. LOAD DATASET (LOCAL PATH)
+# ============================
+base_path = r"E:\Deakin\T3\Sit764\dataset\SisFall_dataset"
+
+all_data = []
+
+for subject_folder in os.listdir(base_path):
+    subject_path = os.path.join(base_path, subject_folder)
+
+    if os.path.isdir(subject_path):
+        print(f"Processing folder: {subject_folder}")
+
+        for filename in os.listdir(subject_path):
+            if filename.endswith(".txt"):
+                filepath = os.path.join(subject_path, filename)
+
+                label = 1 if filename.startswith("F") else 0
+
+                try:
+                    df = pd.read_csv(
+                        filepath,
+                        header=None,
+                        sep=r'[,;\s]+',
+                        engine='python',
+                        on_bad_lines='skip'
+                    )
+
+                    df = df.iloc[:, :9]
+                    df = df.apply(pd.to_numeric, errors='coerce')
+                    df.dropna(how='all', inplace=True)
+
+                    if df.shape[1] == 9 and len(df) > 0:
+                        df["label"] = label
+                        all_data.append(df)
+
+                except Exception as e:
+                    print(f"Error reading {filepath}: {e}")
+
+full_dataset_df = pd.concat(all_data, ignore_index=True)
+
+print("\nDataset Loaded Successfully!")
+print("Original Shape:", full_dataset_df.shape)
+
+# ============================
+# 2. TAKE SAMPLE FOR SPEED
+# ============================
+full_dataset_df = full_dataset_df.sample(n=200000, random_state=42)
+
+print("Sampled Shape:", full_dataset_df.shape)
+
+# ============================
+# 3. DATA CLEANING
+# ============================
+sensor_cols = full_dataset_df.columns[:9]
+
+full_dataset_df[sensor_cols] = full_dataset_df[sensor_cols].apply(pd.to_numeric, errors='coerce')
+full_dataset_df.dropna(subset=sensor_cols, how='all', inplace=True)
+
+full_dataset_df = full_dataset_df.iloc[:, :9].copy().assign(label=full_dataset_df["label"])
+
+full_dataset_df.columns = [
+    "acc1_x", "acc1_y", "acc1_z",
+    "gyro_x", "gyro_y", "gyro_z",
+    "acc2_x", "acc2_y", "acc2_z",
+    "label"
+]
+
+print("After cleaning:", full_dataset_df.shape)
+print(full_dataset_df["label"].value_counts())
+
+# ============================
+# 4. FEATURE + TARGET
+# ============================
+X = full_dataset_df.drop("label", axis=1)
+y = full_dataset_df["label"]
+
+# ============================
+# 5. TRAIN TEST SPLIT
+# ============================
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.3, random_state=42, stratify=y
+)
+
+# ============================
+# 6. SCALING
+# ============================
+scaler = StandardScaler()
+X_train_scaled = scaler.fit_transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+# ============================
+# 7. MODELS
+# ============================
+rf = RandomForestClassifier(n_estimators=100, random_state=42, n_jobs=-1)
+svm = SVC(kernel="rbf")
+knn = KNeighborsClassifier(n_neighbors=5)
+
+print("\nTraining Random Forest...")
+rf.fit(X_train_scaled, y_train)
+
+print("Training SVM...")
+svm.fit(X_train_scaled, y_train)
+
+print("Training KNN...")
+knn.fit(X_train_scaled, y_train)
+
+# ============================
+# 8. PREDICTIONS
+# ============================
+pred_rf = rf.predict(X_test_scaled)
+pred_svm = svm.predict(X_test_scaled)
+pred_knn = knn.predict(X_test_scaled)
+
+# ============================
+# 9. ACCURACY
+# ============================
+acc_rf = accuracy_score(y_test, pred_rf)
+acc_svm = accuracy_score(y_test, pred_svm)
+acc_knn = accuracy_score(y_test, pred_knn)
+
+print("\nAccuracy:")
+print("Random Forest:", acc_rf)
+print("SVM:", acc_svm)
+print("KNN:", acc_knn)
+
+# ============================
+# 10. BEST MODEL
+# ============================
+results = [
+    ("Random Forest", acc_rf, pred_rf),
+    ("SVM", acc_svm, pred_svm),
+    ("KNN", acc_knn, pred_knn)
+]
+
+best_model = max(results, key=lambda x: x[1])
+
+print("\nBest Model:", best_model[0])
+
+# ============================
+# 11. CLASSIFICATION REPORT
+# ============================
+print("\nClassification Report:")
+print(classification_report(y_test, best_model[2], target_names=["Normal", "Fall"]))
+
+# ============================
+# 12. CONFUSION MATRIX
+# ============================
+cm = confusion_matrix(y_test, best_model[2])
+
+disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=["Normal", "Fall"])
+disp.plot()
+plt.title(f"Confusion Matrix - {best_model[0]}")
+plt.show()
+
+# ============================
+# 13. ACCURACY GRAPH
+# ============================
+models = ["Random Forest", "SVM", "KNN"]
+accuracies = [acc_rf, acc_svm, acc_knn]
+
+plt.figure(figsize=(8, 5))
+bars = plt.bar(models, accuracies)
+plt.title("Model Accuracy Comparison - SisFall")
+plt.xlabel("Model")
+plt.ylabel("Accuracy")
+plt.ylim(0, 1)
+
+for bar in bars:
+    h = bar.get_height()
+    plt.text(bar.get_x() + bar.get_width()/2, h + 0.01, f"{h:.4f}",
+             ha="center", va="bottom")
+
+plt.show()