diff --git a/solutions/predcation_of_electric_cars_solution.py b/solutions/predcation_of_electric_cars_solution.py
deleted file mode 100644
index 833ab9c84..000000000
--- a/solutions/predcation_of_electric_cars_solution.py
+++ /dev/null
@@ -1,173 +0,0 @@
-# electric_car_prices_analysis.py
-
-
-def load_data():
-    """
-    Load a predefined electric car dataset.
-
-    Returns:
-        pd.DataFrame: Loaded dataset as a Pandas DataFrame.
-    """
-    import pandas as pd
-    from io import StringIO
-
-    try:
-        # Embedded electric car dataset
-        data = """Make,Model,Year,BatteryCapacity_kWh,Range_km,Price
-Tesla,Model 3,2021,75,450,50000
-Nissan,Leaf,2019,40,240,30000
-Chevrolet,Bolt EV,2020,66,380,37000
-Hyundai,Kona Electric,2021,64,415,40000
-Volkswagen,ID.4,2022,77,520,45000
-BMW,i3,2018,33,200,29000
-"""
-        return pd.read_csv(StringIO(data))
-    except Exception as e:
-        print(f"Error loading data: {e}")
-        return pd.DataFrame()
-
-
-def preprocess_data(data):
-    """
-    Preprocess the electric car dataset by handling missing values and encoding categorical variables.
-    """
-    import pandas as pd
-
-    try:
-        data = data.dropna()
-        # One-hot encode 'Make' and 'Model'
-        data = pd.get_dummies(data, columns=["Make", "Model"], drop_first=True)
-        return data
-    except Exception as e:
-        print(f"Error preprocessing data: {e}")
-        return pd.DataFrame()
-
-
-def analyze_data(data):
-    """
-    Perform exploratory data analysis on the dataset.
-    """
-    import matplotlib.pyplot as plt
-    import seaborn as sns
-
-    try:
-        print("Dataset Summary:")
-        print(data.describe())
-
-        sns.pairplot(data[["Year", "BatteryCapacity_kWh", "Range_km", "Price"]])
-        plt.show()
-    except Exception as e:
-        print(f"Error in data analysis: {e}")
-
-
-def train_model(data):
-    """
-    Train a predictive model using the dataset.
-
-    Returns:
-        tuple: Trained model, test features, test labels
-    """
-    from sklearn.model_selection import train_test_split
-    from sklearn.linear_model import LinearRegression
-    from sklearn.preprocessing import StandardScaler
-
-    try:
-        features = data.drop(columns="Price")
-        target = data["Price"]
-
-        X_train, X_test, y_train, y_test = train_test_split(
-            features, target, test_size=0.2, random_state=42
-        )
-
-        # Scale the features
-        scaler = StandardScaler()
-        X_train = scaler.fit_transform(X_train)
-        X_test = scaler.transform(X_test)
-
-        model = LinearRegression()
-        model.fit(X_train, y_train)
-
-        return model, X_test, y_test, scaler
-    except Exception as e:
-        print(f"Error in model training: {e}")
-        return None, None, None, None
-
-
-def evaluate_model(model, X_test, y_test):
-    """
-    Evaluate the trained model using Mean Squared Error and R-squared metrics.
-    """
-    from sklearn.metrics import mean_squared_error, r2_score
-
-    try:
-        y_pred = model.predict(X_test)
-
-        mse = mean_squared_error(y_test, y_pred)
-        r2 = r2_score(y_test, y_pred)
-
-        print(f"Mean Squared Error: {mse}")
-        print(f"R-squared: {r2}")
-    except Exception as e:
-        print(f"Error in model evaluation: {e}")
-
-
-def visualize_results(model, X_test, y_test):
-    """
-    Visualize the actual vs predicted prices and feature importance.
-    """
-    import matplotlib.pyplot as plt
-
-    try:
-        # Actual vs Predicted Prices
-        y_pred = model.predict(X_test)
-        plt.figure(figsize=(10, 6))
-        plt.scatter(y_test, y_pred, alpha=0.6, color="blue")
-        plt.plot(
-            [y_test.min(), y_test.max()],
-            [y_test.min(), y_test.max()],
-            "--r",
-            linewidth=2,
-        )
-        plt.xlabel("Actual Prices")
-        plt.ylabel("Predicted Prices")
-        plt.title("Actual vs Predicted Prices")
-        plt.show()
-
-        # Feature Coefficients
-        coefficients = model.coef_
-        feature_names = ["Year", "BatteryCapacity_kWh", "Range_km"] + list(
-            X_test.columns[3:]
-        )
-        plt.figure(figsize=(8, 4))
-        plt.bar(feature_names, coefficients, color="skyblue")
-        plt.title("Feature Coefficients")
-        plt.xticks(rotation=45)
-        plt.ylabel("Coefficient Value")
-        plt.show()
-    except Exception as e:
-        print(f"Error in visualization: {e}")
-
-
-def save_model(model, scaler, filename="electric_car_price_model.pkl"):
-    """
-    Save the trained model and scaler to a file.
-    """
-    import joblib
-
-    try:
-        joblib.dump({"model": model, "scaler": scaler}, filename)
-        print(f"Model saved as {filename}")
-    except Exception as e:
-        print(f"Error saving model: {e}")
-
-
-if __name__ == "__main__":
-    data = load_data()
-    if not data.empty:
-        data = preprocess_data(data)
-        analyze_data(data)
-        model, X_test, y_test, scaler = train_model(data)
-        if model:
-            evaluate_model(model, X_test, y_test)
-            visualize_results(model, X_test, y_test)
-            save_model(model, scaler)