lab04.ipynb

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      "source": [
        "Lab 4: Classification and k-Nearest Neighbors \n",
        "\n",
        "Kobie Williams\n",
        "\n",
        "2/15/2022\n",
        "\n",
        "The purpose of this file is to make a K-Nearest Neighbor Classifier and experiment with classifying examples from a survey of Computer Science 1 students."
      ],
      "metadata": {
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      "source": [
        "import numpy as np \n",
        "import matplotlib.pyplot as plt \n",
        "import pandas as pd "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "file_link = 'https://drive.google.com/uc?export=download&id=1872WZovmmIRvyAra5AENBfUuVetA77ag' \n",
        "student_df = pd.read_csv(file_link)\n",
        "student_df.head(6)"
      ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Textbook</th>\n",
              "      <th>Videos</th>\n",
              "      <th>Lecture</th>\n",
              "      <th>Discussion</th>\n",
              "      <th>Problems</th>\n",
              "      <th>Proactive</th>\n",
              "      <th>CodeExperience</th>\n",
              "      <th>MotivatedByFun</th>\n",
              "      <th>MotivatedByChallenge</th>\n",
              "      <th>MotivatedByGrades</th>\n",
              "      <th>MotivatedByJob</th>\n",
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            "text/plain": [
              "   ID  Textbook  Videos  ...  MotivatedByGrades  MotivatedByJob  Grade\n",
              "0   1         3       4  ...                  4               4      D\n",
              "1   2         2       3  ...                  4               4      B\n",
              "2   3         4       5  ...                  5               5      C\n",
              "3   4         1       5  ...                  1               3      F\n",
              "4   5         4       3  ...                  5               5      B\n",
              "5   6         2       3  ...                  4               3      D\n",
              "\n",
              "[6 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "#separate out the data (X)  \n",
        "X = student_df[['Textbook', 'Videos', 'Lecture', 'Discussion', 'Problems', 'Proactive', 'CodeExperience', 'MotivatedByFun', 'MotivatedByChallenge', 'MotivatedByGrades', 'MotivatedByJob']]\n",
        "\n",
        "#target variable is grade\n",
        "y = student_df['Grade']\n"
      ],
      "metadata": {
        "id": "LeIvAxaBuHdJ"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Splitting data and labes into training and testing sets \n",
        "#random state to a known value of 13 to keep values reproducible and predictable \n",
        "Xtrain, Xtest, ytrain, ytest = train_test_split(X,y,test_size=0.5, random_state = 13)"
      ],
      "metadata": {
        "id": "h4wi5a9RBLXC"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#number of neighbors is k (\"n_neighbors\") \n",
        "#distance metric is the euclidean distance \n",
        "from sklearn.neighbors import KNeighborsClassifier \n",
        "model = KNeighborsClassifier(n_neighbors=4, metric = 'euclidean') \n",
        "model.fit(Xtrain,ytrain) \n"
      ],
      "metadata": {
        "id": "nVTqenoRC0Fg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db9aa9f0-38f5-470d-8040-784296eb4141"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(metric='euclidean', n_neighbors=4)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Compare the model predictions on the test set vs the actual ('ground truth') labels we can measure the model accuracy \n",
        "\n",
        "ypred = model.predict(Xtest) \n",
        "ypred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wdudGOEdeXWd",
        "outputId": "5e0faaf3-5466-4323-cdc5-ed895c6cee1c"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['C', 'B', 'B', 'A', 'C', 'D', 'C', 'C', 'C', 'B', 'C', 'C', 'C',\n",
              "       'C', 'C', 'C', 'C', 'C', 'C', 'C', 'D', 'A', 'A', 'C', 'C', 'C',\n",
              "       'C', 'C'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ypred == ytest"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bR_Kjnjqec8K",
        "outputId": "7535b96a-f070-45d9-9735-fb8a74635b3c"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29     True\n",
              "4      True\n",
              "23     True\n",
              "51     True\n",
              "0     False\n",
              "14    False\n",
              "40    False\n",
              "53    False\n",
              "49    False\n",
              "6      True\n",
              "27     True\n",
              "45    False\n",
              "41    False\n",
              "28    False\n",
              "15     True\n",
              "31    False\n",
              "42    False\n",
              "17     True\n",
              "5     False\n",
              "3     False\n",
              "7     False\n",
              "20     True\n",
              "50     True\n",
              "43    False\n",
              "12    False\n",
              "8     False\n",
              "30    False\n",
              "24    False\n",
              "Name: Grade, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score \n",
        "accuracy_score(ytest, ypred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2SQrWpkvegj7",
        "outputId": "2434e59f-3018-4dcf-ac55-00eb8bdc8cfc"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.35714285714285715"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#confusion matrix \n",
        "from sklearn.metrics import confusion_matrix \n",
        "cm = confusion_matrix(ytest, ypred) "
      ],
      "metadata": {
        "id": "6RveNosBik4P"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns \n",
        "sns.heatmap(cm, square=True, annot=True, fmt='d', cbar=True, cmap=\"PuBu\", \n",
        "            xticklabels=model.classes_, \n",
        "            yticklabels=model.classes_) \n",
        "plt.xlabel('Predicted Label'); \n",
        "plt.ylabel('True label')\n",
        "plt.title('CS Student Classification Confusion Matrix')\n",
        "plt.show() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "O4YxlHA9jain",
        "outputId": "7468a90b-f982-4556-8c69-daac53af54ca"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "i. The accuracy is not that good in my opinion because it is only giving around 35% accuracy. But compared to random choice, this is way better. Trying to do random choice with this many variables to calculate could be challenging \n",
        "ii. The model is best at determining outcomes for the students in the middle ranges of grades and not so much for the outlier ranges of grades. Middle ranges = B,C,D. Outliers = A, F"
      ],
      "metadata": {
        "id": "Uie1H_IAnsZ6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "EGxhZfTnoLsF"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}