HCIA-AI_Simple-Project_G-6_Aly El-Deen Yasser Ali .ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyO6aJiVlcT69Gqf/R+4Tiyn"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["\n","\n","- My name: Aly El-Deen Yasser Ali\n","- Type of project: Simple Project\n","- Group : G-6\n","\n","\n","## The problem I try to solve:\n","  is to make a model that can classify the types of iris flower by using KNN and SVM and it is important as a train for me as a beginner to practice on building models that can analyse the data like this\n","\n","## Objectives of assignment:\n","is to train a SVM and KNN model to solve this problem with a good accuracy\n","\n","## Expected Output:\n","A clean and well-structured dataset ready for model training and trained KNN and SVM models\n","\n"],"metadata":{"id":"G4Nu5A1wiNFr"}},{"cell_type":"code","execution_count":16,"metadata":{"id":"GzMN71YNM_gF","executionInfo":{"status":"ok","timestamp":1721689948070,"user_tz":-180,"elapsed":337,"user":{"displayName":"Aly eldeen yasser Aly Elbadry","userId":"05034867470524365093"}}},"outputs":[],"source":["# Source : https://www.geeksforgeeks.org/iris-dataset/\n","# The Data set I used is : Iris Data set\n","# Small brief about it : The Iris dataset is one of the most well-known and commonly used datasets in the field of machine learning.\n","# It is a 150 samples of iris flowers Data set from three different species: Setosa, Versicolor, and Virginica.\n","# Each sample includes four features: sepal length, sepal width, petal length, and petal width.\n","\n","from sklearn.datasets import load_iris\n","import pandas as pd\n","import numpy as np\n","import seaborn as sns\n","from sklearn.model_selection import train_test_split\n","from collections import Counter\n","from sklearn.svm import SVC\n","from sklearn.decomposition import PCA\n","import matplotlib.pyplot as plt\n","from sklearn.metrics import accuracy_score, classification_report\n","from sklearn.preprocessing import StandardScaler"]},{"cell_type":"markdown","source":["## First Step\n","To load the data of data set and check if there is any missing values or any other outliers"],"metadata":{"id":"XhhKL4THmTDY"}},{"cell_type":"code","source":["# Load the Iris dataset\n","iris = load_iris()\n","data = pd.DataFrame(iris.data, columns=iris.feature_names)\n","data['Target'] = iris.target\n","# Print the feature names and target names\n","print(\"Feature names:\", iris.feature_names)\n","print(\"Target names:\", iris.target_names)\n","print(data.head())\n","data"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":684},"id":"vYNhwtQPmI9z","executionInfo":{"status":"ok","timestamp":1721689948650,"user_tz":-180,"elapsed":6,"user":{"displayName":"Aly eldeen yasser Aly 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length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n","0                  5.1               3.5                1.4               0.2   \n","1                  4.9               3.0                1.4               0.2   \n","2                  4.7               3.2                1.3               0.2   \n","3                  4.6               3.1                1.5               0.2   \n","4                  5.0               3.6                1.4               0.2   \n","..                 ...               ...                ...               ...   \n","145                6.7               3.0                5.2               2.3   \n","146                6.3               2.5                5.0               1.9   \n","147                6.5               3.0                5.2               2.0   \n","148                6.2               3.4                5.4               2.3   \n","149                5.9               3.0                5.1               1.8   \n","\n","     Target  \n","0         0  \n","1         0  \n","2         0  \n","3         0  \n","4         0  \n","..      ...  \n","145       2  \n","146       2  \n","147       2  \n","148       2  \n","149       2  \n","\n","[150 rows x 5 columns]"],"text/html":["\n","  <div id=\"df-01b7b4ae-4d0a-4921-b258-7ac9ce26dcc2\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sepal length (cm)</th>\n","      <th>sepal width (cm)</th>\n","      <th>petal length (cm)</th>\n","      <th>petal width (cm)</th>\n","      <th>Target</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n"," 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\"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":17}]},{"cell_type":"code","source":["# Check for missing values\n","print(data.isnull().sum())\n","\n","# Check for outliers\n","sns.heatmap(data.isnull(),yticklabels=False, annot=True)\n","# As we see the data is perfect and doesn t need to be cleaned by any method"],"metadata":{"id":"-AfVgnwNmUwn","colab":{"base_uri":"https://localhost:8080/","height":665},"executionInfo":{"status":"ok","timestamp":1721689951250,"user_tz":-180,"elapsed":2604,"user":{"displayName":"Aly eldeen yasser Aly Elbadry","userId":"05034867470524365093"}},"outputId":"3277ed2b-3796-4cbc-b424-81addd636ef8"},"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["sepal length (cm)    0\n","sepal width (cm)     0\n","petal length (cm)    0\n","petal width (cm)     0\n","Target               0\n","dtype: int64\n"]},{"output_type":"execute_result","data":{"text/plain":["<Axes: >"]},"metadata":{},"execution_count":18},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# Second Step\n","As we see the data doesn't need cleaning so we will start data analyse the data using KNN first then SVM then compare between their values"],"metadata":{"id":"cZykPwTXpeyF"}},{"cell_type":"code","source":["# Feature Selection\n","x = data.drop('Target', axis=1)\n","y = data['Target']\n","\n","# First we will split data\n","x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n","\n","# To convert them to numpy\n","x_train = x_train.to_numpy()\n","x_test = x_test.to_numpy()\n","y_train = y_train.to_numpy()\n","y_test = y_test.to_numpy()"],"metadata":{"id":"UJ2vyc9fp3WG","executionInfo":{"status":"ok","timestamp":1721689951250,"user_tz":-180,"elapsed":16,"user":{"displayName":"Aly eldeen yasser Aly Elbadry","userId":"05034867470524365093"}}},"execution_count":19,"outputs":[]},{"cell_type":"markdown","source":["## Third Step\n","### KNN Model\n","KNN brief: It is one of machine learning model that is used to classify data by calculation the distance between the data needed to be classified and the nearest K data around it"],"metadata":{"id":"Mf6SzG5OKEbw"}},{"cell_type":"code","source":["# Function to calculate distance between the points\n","def calcDistance(a, b):\n","  return np.sqrt(np.sum(a - b)**2)"],"metadata":{"id":"lJHGWycYds5X","executionInfo":{"status":"ok","timestamp":1721689951251,"user_tz":-180,"elapsed":15,"user":{"displayName":"Aly eldeen yasser Aly Elbadry","userId":"05034867470524365093"}}},"execution_count":20,"outputs":[]},{"cell_type":"code","source":["def singlePrediction(x_train, y_train, x_test, k):\n","  # Calculate the distance between the test point and all the training points\n","  distance = np.array([calcDistance(x_test, x) for x in x_train])\n","  # Get the indices of the k nearest neighbors\n","  k_indices = np.argsort(distance)[:k]\n","  k_nearest_labels = [y_train[i] for i in k_indices]\n","  most_common = Counter(k_nearest_labels).most_common(1)\n","  return most_common[0][0]"],"metadata":{"id":"4CuznUVLnC41","executionInfo":{"status":"ok","timestamp":1721689951251,"user_tz":-180,"elapsed":14,"user":{"displayName":"Aly eldeen yasser Aly Elbadry","userId":"05034867470524365093"}}},"execution_count":21,"outputs":[]},{"cell_type":"code","source":["def predict(x_train, y_train, x_test, k):\n","  prediction = np.array([])\n","  for i in x_test:\n","    prediction = np.append(prediction , singlePrediction(x_train, y_train, i, k))\n","  return np.array(prediction)"],"metadata":{"id":"IPYuUn-Eds0L","executionInfo":{"status":"ok","timestamp":1721689951251,"user_tz":-180,"elapsed":13,"user":{"displayName":"Aly eldeen yasser Aly Elbadry","userId":"05034867470524365093"}}},"execution_count":22,"outputs":[]},{"cell_type":"code","source":["# To Get the best Value in KNN with Best K\n","final_pre = np.array([])\n","final_accu = -1\n","final_k = -1\n","\n","# K from 1 to 10\n","for i in range(1, 11):\n","  # Prediction done in each k\n","  prediction = predict(x_train, y_train, x_test, i)\n","  accuracy = np.mean(prediction == y_test)\n","\n","  # To Compare the values of Accuracy\n","  if accuracy > final_accu:\n","    final_k = i\n","    final_accu = accuracy\n","    final_pre = prediction\n","\n","# To Print the Best Value of KNN\n","print(\"Best K in KNN:\", final_k)\n","print(\"Best Accuracy in KNN:\", final_accu)\n","print(f\"Best Predictions: {final_pre}\")\n","print(f\"Actual labels: {y_test}\")\n","print(\"Classification Report:\")\n","print(classification_report(y_test, final_pre))\n"],"metadata":{"id":"tYzclIfduIXw","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1721689951252,"user_tz":-180,"elapsed":13,"user":{"displayName":"Aly eldeen yasser Aly Elbadry","userId":"05034867470524365093"}},"outputId":"0f75a890-bb91-4d6d-dd52-13d8cf210db8"},"execution_count":23,"outputs":[{"output_type":"stream","name":"stdout","text":["Best K in KNN: 9\n","Best Accuracy in KNN: 0.9333333333333333\n","Best Predictions: [1. 0. 2. 1. 2. 0. 1. 2. 1. 1. 2. 0. 0. 0. 0. 2. 2. 1. 1. 2. 0. 2. 0. 2.\n"," 2. 2. 2. 2. 0. 0.]\n","Actual labels: [1 0 2 1 1 0 1 2 1 1 2 0 0 0 0 1 2 1 1 2 0 2 0 2 2 2 2 2 0 0]\n","Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       1.00      1.00      1.00        10\n","           1       1.00      0.78      0.88         9\n","           2       0.85      1.00      0.92        11\n","\n","    accuracy                           0.93        30\n","   macro avg       0.95      0.93      0.93        30\n","weighted avg       0.94      0.93      0.93        30\n","\n"]}]},{"cell_type":"markdown","source":["## Fourth Step\n","### SVM Model\n","SVM brief : It is a machine learning model that use a linear model and try to find linear decision that best seprates data. The best hyperplane is the one that yields the largest separation between both classes."],"metadata":{"id":"6teJ93MYGODB"}},{"cell_type":"code","source":["# SVM code train and prediction\n","svm = SVC(kernel='linear', C=1.0, random_state=0)\n","svm.fit(x_train, y_train)\n","predict = svm.predict(x_test)"],"metadata":{"id":"-7GYxXuQGOM9","executionInfo":{"status":"ok","timestamp":1721689951252,"user_tz":-180,"elapsed":12,"user":{"displayName":"Aly eldeen yasser Aly Elbadry","userId":"05034867470524365093"}}},"execution_count":24,"outputs":[]},{"cell_type":"code","source":["# Evaluate the model\n","accuracy = accuracy_score(y_test, predict)\n","print(f\"Accuracy: {accuracy}\")\n","print(\"Classification Report:\")\n","print(classification_report(y_test, predict))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ti68pwXUx9vu","executionInfo":{"status":"ok","timestamp":1721689951252,"user_tz":-180,"elapsed":11,"user":{"displayName":"Aly eldeen yasser Aly Elbadry","userId":"05034867470524365093"}},"outputId":"69afea42-0e7f-46e2-dc5f-4b652f4c1271"},"execution_count":25,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy: 1.0\n","Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       1.00      1.00      1.00        10\n","           1       1.00      1.00      1.00         9\n","           2       1.00      1.00      1.00        11\n","\n","    accuracy                           1.00        30\n","   macro avg       1.00      1.00      1.00        30\n","weighted avg       1.00      1.00      1.00        30\n","\n"]}]},{"cell_type":"markdown","source":["### To divided the element and put them on a graph\n","we will need to make reduce dimensionality to 2D for visualization"],"metadata":{"id":"0ZoWezI2qMA3"}},{"cell_type":"code","source":["# Plot decision boundaries\n","def plotDecBound(x, y, model, title):\n","    x_min = x[:, 0].min() - 1\n","    y_min=  x[:, 1].min() - 1\n","    x_max = x[:, 0].max() + 1\n","    y_max = x[:, 1].max() + 1\n","\n","    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.01), np.arange(y_min, y_max, 0.01))\n","\n","    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n","    Z = Z.reshape(xx.shape)\n","    plt.contourf(xx, yy, Z, alpha=0.8)\n","    plt.scatter(x[:, 0], x[:, 1], c=y, edgecolors='k', marker='o')\n","    plt.title(title)\n","    plt.show()"],"metadata":{"id":"FHLCQda-nGTn","executionInfo":{"status":"ok","timestamp":1721689951253,"user_tz":-180,"elapsed":9,"user":{"displayName":"Aly eldeen yasser Aly Elbadry","userId":"05034867470524365093"}}},"execution_count":26,"outputs":[]},{"cell_type":"code","source":["# Reduce dimensionality to 2D for visualization\n","pca = PCA(n_components=2)\n","x_pca = pca.fit_transform(x)\n","\n","# Split PCA-transformed data\n","x_train_pca, x_test_pca, y_train_pca, y_test_pca = train_test_split(x_pca, y, test_size=0.2, random_state=42)\n","\n","# Train SVM on PCA-transformed data\n","svm_pca = SVC(kernel='linear', C=1.0, random_state=0)\n","svm_pca.fit(x_train_pca, y_train_pca)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":74},"id":"Sk8kqkAzsZdm","executionInfo":{"status":"ok","timestamp":1721689951720,"user_tz":-180,"elapsed":475,"user":{"displayName":"Aly eldeen yasser Aly Elbadry","userId":"05034867470524365093"}},"outputId":"a84fc19b-319f-4df7-dcf7-ef6267a33f9c"},"execution_count":27,"outputs":[{"output_type":"execute_result","data":{"text/plain":["SVC(kernel='linear', random_state=0)"],"text/html":["<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;, random_state=0)</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":27}]},{"cell_type":"code","source":["# To show the x_train figure\n","plotDecBound(x_train_pca, y_train_pca, svm_pca, \"SVM Decision Boundaries (Training Set)\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":452},"id":"5KkAaofXsQZa","executionInfo":{"status":"ok","timestamp":1721689952308,"user_tz":-180,"elapsed":592,"user":{"displayName":"Aly eldeen yasser Aly Elbadry","userId":"05034867470524365093"}},"outputId":"d9570a8b-7ea3-40c1-900a-d837ab7dc247"},"execution_count":28,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# To show the x_test figure\n","plotDecBound(x_test_pca, y_test_pca, svm_pca, \"SVM Decision Boundaries (Test Set)\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":452},"id":"iJgMk8AqsQlA","executionInfo":{"status":"ok","timestamp":1721689953368,"user_tz":-180,"elapsed":1064,"user":{"displayName":"Aly eldeen yasser Aly Elbadry","userId":"05034867470524365093"}},"outputId":"236b1b49-0d9b-4367-d332-b818695d839c"},"execution_count":29,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# Reference"],"metadata":{"id":"I-Z5k22_uEIv"}},{"cell_type":"code","source":["# =============================================================================== References ===================================================================================\n","                                                            # 1st Reference : https://www.youtube.com/watch?v=OS2m0f2gVJ0\n","                                                            # 2nd Reference : https://www.youtube.com/watch?v=yHC_1BiVy8E\n","                                                            # 3rd Reference : https://www.geeksforgeeks.org/iris-dataset/\n","                                                            # 4th Reference : https://www.youtube.com/watch?v=Q7vT0--5VII&t=36s"],"metadata":{"id":"mWG8WtGdkq0_","executionInfo":{"status":"ok","timestamp":1721689953369,"user_tz":-180,"elapsed":8,"user":{"displayName":"Aly eldeen yasser Aly Elbadry","userId":"05034867470524365093"}}},"execution_count":30,"outputs":[]}]}