diff --git a/Solution.py b/Solution.py
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+++ b/Solution.py
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+
+
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.metrics import accuracy_score, classification_report
+import pandas as pd
+
+def load_data():
+    iris = load_iris()
+    X = iris.data
+    y = iris.target
+
+    df = pd.DataFrame(X, columns=iris.feature_names)
+    df["species"] = y
+
+    print("First 5 rows of data:")
+    print(df.head())
+    print("\nFeature names:", iris.feature_names)
+    print("Target names:", iris.target_names)
+    print("\nShape of data:", df.shape)
+
+    return df, iris
+
+def train_test_split_data(df):
+    X = df.drop("species", axis=1)
+    y = df["species"]
+
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.2, random_state=42
+    )
+
+    print("\nTrain shape:", X_train.shape, "Test shape:", X_test.shape)
+    return X_train, X_test, y_train, y_test
+
+def train_logistic_regression(X_train, y_train):
+    log_reg = LogisticRegression(max_iter=200)
+    log_reg.fit(X_train, y_train)
+    print("\nLogistic Regression training complete.")
+    return log_reg
+
+def evaluate_model(model, X_test, y_test, model_name="Model"):
+    y_pred = model.predict(X_test)
+    acc = accuracy_score(y_test, y_pred)
+
+    print(f"\n=== {model_name} Evaluation ===")
+    print("Accuracy:", acc)
+    print("\nClassification report:")
+    print(classification_report(y_test, y_pred))
+
+    print("Sample predictions vs true:")
+    for i in range(min(5, len(y_test))):
+        print(f"Predicted: {y_pred[i]}, True: {y_test.iloc[i]}")
+    return acc
+
+def train_knn(X_train, y_train, n_neighbors=5):
+    knn = KNeighborsClassifier(n_neighbors=n_neighbors)
+    knn.fit(X_train, y_train)
+    print(f"\nKNN (k={n_neighbors}) training complete.")
+    return knn
+
+def main():
+    df, iris = load_data()
+    X_train, X_test, y_train, y_test = train_test_split_data(df)
+
+    #Logistic Regression
+    log_reg_model = train_logistic_regression(X_train, y_train)
+    log_reg_acc = evaluate_model(log_reg_model, X_test, y_test, "Logistic Regression")
+
+    #KNN
+    knn_model = train_knn(X_train, y_train, n_neighbors=5)
+    knn_acc = evaluate_model(knn_model, X_test, y_test, "KNN (k=5)")
+
+    print("\nSummary:")
+    print(f"Logistic Regression accuracy: {log_reg_acc:.4f}")
+    print(f"KNN (k=5) accuracy: {knn_acc:.4f}")
+
+    if knn_acc > log_reg_acc:
+        print("KNN performed better on this test split.")
+    elif knn_acc < log_reg_acc:
+        print("Logistic Regression performed better on this test split.")
+    else:
+        print("Both models performed equally on this test split.")
+
+if __name__ == "__main__":
+    main()
diff --git a/excercise.md b/excercise.md
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+++ b/excercise.md
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+# ML Learning Project – Iris Classification (Exercise)
+
+## 1. Overview
+
+In this exercise, you will build a **Machine Learning model** to classify iris flowers into three species using the classic **Iris dataset**.
+
+You will:
+- Load and explore data
+- Split into train/test
+- Train a model
+- Evaluate performance
+- Try simple improvements
+
+---
+
+## 2. Objective
+
+> Build a classifier that predicts the iris flower species based on petal and sepal measurements.
+
+Target variable:  
+- `species` (Setosa, Versicolor, Virginica)
+
+Features:
+- `sepal length`
+- `sepal width`
+- `petal length`
+- `petal width`
+
+We will use the built-in Iris dataset from **scikit-learn** (no external CSV needed).
+
+---
+
+## 3. Requirements
+
+### Tools / Libraries
+- Python 3.x
+- `scikit-learn`
+- `pandas`
+- `numpy`
+- `matplotlib` (optional, for plots)
+
+Install (if needed):
+
+```