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diff --git a/algorithm.py b/algorithm.py
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index 0000000..174e366
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+++ b/algorithm.py
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+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report
+import csv
+
+data = {
+    "speed": [],
+    "timestamp": [],
+    "supposedActivity": []
+}
+
+with open('user_activity.csv', newline='', encoding='utf-8') as csvfile:
+    sr = csv.reader(csvfile, delimiter=',')
+    next(sr)  # Skip header
+    for row in sr:
+        data['speed'].append(row[1])
+        data['timestamp'].append(row[2])
+        data['supposedActivity'].append(row[3])
+
+df = pd.DataFrame(data)
+
+# Ensure 'speed' is numeric (convert from string to float)
+df["speed"] = pd.to_numeric(df["speed"], errors="coerce")
+
+# Convert 'timestamp' to datetime
+df["timestamp"] = pd.to_datetime(df["timestamp"], errors="coerce")
+
+# Extract features from 'timestamp'
+df["hour_of_day"] = df["timestamp"].dt.hour
+df["day_of_week"] = df["timestamp"].dt.weekday
+
+# Encode 'supposedActivity' manually
+activity_mapping = {"STILL": 0, "WALKING": 1, "RUNNING": 2, "IN_VEHICLE": 3}
+df["activity_encoded"] = df["supposedActivity"].map(activity_mapping)
+
+# Remove rows with missing values
+df = df.dropna()
+
+# Prepare features (X) and target (y)
+X = df[["speed", "hour_of_day", "day_of_week"]]
+# X = df[["speed"]]
+y = df["activity_encoded"].astype(int)
+
+# Train-test split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Train Random Forest Classifier
+clf = RandomForestClassifier(random_state=42)
+clf.fit(X_train, y_train)
+
+# Predict and print classification report
+y_pred = clf.predict(X_test)
+print("Classification Report:")
+print(classification_report(y_test, y_pred))
+
+new_speed = float(input("Add speed: "))
+hour_of_day = int(input("Add hour of the day: "))
+day_of_week = int(input("Add day of the week: "))
+
+# X_new = [[new_speed, hour_of_day, day_of_week]]
+X_new = pd.DataFrame([[new_speed, hour_of_day, day_of_week]], columns=["speed",
+                                                                       "hour_of_day",
+                                                                       "day_of_week"])
+# X_new = pd.DataFrame([[new_speed]], columns=["speed"])
+
+# Predict activity
+predicted_activity_encoded = clf.predict(X_new)[0]
+activity_labels = {v: k for k, v in activity_mapping.items()}
+predicted_activity = activity_labels.get(predicted_activity_encoded, "UNKNOWN")
+
+print(f"Predicted activity for speed {new_speed} m/s: {predicted_activity}")