DA_6.ipynb

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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VD2bB890ELFL"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "LR=LinearRegression()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t=[[5],[7],[12],[16],[20]] #time independent 2D\n",
        "m=[40,120,180,210,240] #mass dependent\n",
        "LR.fit(t,m)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "kiQCE7rBEaHL",
        "outputId": "d3ffd68a-0660-4b01-8c18-b09a01b206e2"
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          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
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              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "LR.predict([[5.5]]) #prediction by 2D array"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b89YghxyEfDl",
        "outputId": "e3700d48-fa9e-4b4e-df6d-c18332b502fb"
      },
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([78.64935065])"
            ]
          },
          "metadata": {},
          "execution_count": 3
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "LR.predict([[25]]) #prediction by 2D array"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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          "data": {
            "text/plain": [
              "array([316.7012987])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import numpy as np\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Distance and corresponding probability data\n",
        "distances = np.array([1,2,5,10,15,20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,35,40,41,47,50]).reshape(-1, 1)\n",
        "probabilities = np.array([1,1,1,1,1,1,0.9, 0.85, 0.73, 0.67, 0.5, 0.47, 0.39, 0.31, 0.25, 0.15,0,0,0,0,0])\n",
        "\n",
        "#convert probabilities to binary labels\n",
        "threshold=0.5\n",
        "binary_labels=(probabilities > threshold).astype(int)\n",
        "\n",
        "#create and fit logistic regression model\n",
        "logr=LogisticRegression()\n",
        "logr.fit(distances,binary_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
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        "id": "BvuVrm8sEumQ",
        "outputId": "cda36f64-f4f5-4524-b0fb-3ac2bc568946"
      },
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
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              "<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>LogisticRegression()</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\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p=logr.predict([[10]]) #distance\n",
        "print(p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jou4nnZuE22H",
        "outputId": "afb8d757-9b53-4533-aa5f-363935069fcb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if p==[1]:\n",
        "  print(\"Goal\")\n",
        "else:\n",
        "  print(\"No Goal\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ctJRq_yjE9jt",
        "outputId": "da9e1734-301d-434e-9d0b-a243ad8d6d58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Goal\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a=logr.predict([[25]])\n",
        "print(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QfDqRiYLFCOR",
        "outputId": "f05f6091-731d-4ca2-f9f6-dce7e81364b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if a==[1]:\n",
        "  print(\"Goal\")\n",
        "else:\n",
        "  print(\"No Goal\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dEjZ2sgtFJDU",
        "outputId": "4ec648fd-6449-4d05-cc84-729218357e35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No Goal\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Generate distance for prediction\n",
        "dist=np.linspace(1,50,100).reshape(-1,1)\n",
        "print(dist) #distance\n",
        "\n",
        "#take predict using the model\n",
        "prob=logr.predict_proba(dist)[:,1] #predictions\n",
        "print(prob)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M0Ts6pLvFQoY",
        "outputId": "c2d3b239-fabc-4993-b2e7-03b0f096d7f1"
      },
      "execution_count": null,
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          "text": [
            "[[ 1.        ]\n",
            " [ 1.49494949]\n",
            " [ 1.98989899]\n",
            " [ 2.48484848]\n",
            " [ 2.97979798]\n",
            " [ 3.47474747]\n",
            " [ 3.96969697]\n",
            " [ 4.46464646]\n",
            " [ 4.95959596]\n",
            " [ 5.45454545]\n",
            " [ 5.94949495]\n",
            " [ 6.44444444]\n",
            " [ 6.93939394]\n",
            " [ 7.43434343]\n",
            " [ 7.92929293]\n",
            " [ 8.42424242]\n",
            " [ 8.91919192]\n",
            " [ 9.41414141]\n",
            " [ 9.90909091]\n",
            " [10.4040404 ]\n",
            " [10.8989899 ]\n",
            " [11.39393939]\n",
            " [11.88888889]\n",
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            " [15.35353535]\n",
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            " [17.33333333]\n",
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            " [19.80808081]\n",
            " [20.3030303 ]\n",
            " [20.7979798 ]\n",
            " [21.29292929]\n",
            " [21.78787879]\n",
            " [22.28282828]\n",
            " [22.77777778]\n",
            " [23.27272727]\n",
            " [23.76767677]\n",
            " [24.26262626]\n",
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            " [25.25252525]\n",
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            " [28.71717172]\n",
            " [29.21212121]\n",
            " [29.70707071]\n",
            " [30.2020202 ]\n",
            " [30.6969697 ]\n",
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            " [40.1010101 ]\n",
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            " [50.        ]]\n",
            "[1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
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            " 1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            " 9.99999999e-01 9.99999998e-01 9.99999997e-01 9.99999995e-01\n",
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            " 9.98834306e-01 9.97902352e-01 9.96228129e-01 9.93226705e-01\n",
            " 9.87866023e-01 9.78355119e-01 9.61678531e-01 9.33032722e-01\n",
            " 8.85522784e-01 8.11129877e-01 7.04524003e-01 5.69669049e-01\n",
            " 4.23619405e-01 2.89797741e-01 1.84703449e-01 1.11725670e-01\n",
            " 6.52734947e-02 3.73232326e-02 2.10715348e-02 1.18095223e-02\n",
            " 6.59122462e-03 3.67018284e-03 2.04100338e-03 1.13418691e-03\n",
            " 6.30014050e-04 3.49879429e-04 1.94281951e-04 1.07873915e-04\n",
            " 5.98940572e-05 3.32538365e-05 1.84626754e-05 1.02504906e-05\n",
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            " 5.13768131e-08 2.85242100e-08 1.58365320e-08 8.79238183e-09\n",
            " 4.88149664e-09 2.71018819e-09 1.50468608e-09 8.35395932e-10\n",
            " 4.63808613e-10 2.57504761e-10 1.42965654e-10 7.93739822e-11\n",
            " 4.40681301e-11 2.44664566e-11 1.35836827e-11 7.54160848e-12\n",
            " 4.18707206e-12 2.32464633e-12 1.29063471e-12 7.16555436e-13\n",
            " 3.97828826e-13 2.20873036e-13 1.22627861e-13 6.80825177e-14]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plotting actual data -train\n",
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(distances,binary_labels,color='black',label=\"Data\")\n",
        "\n",
        "#plotting test data with predictions -valid/test\n",
        "plt.plot(dist,prob,color='blue',label='Logistic Regression')\n",
        "plt.title(\"Distance vs Probability of scoring a Goal\")\n",
        "plt.xlabel('Distance')\n",
        "plt.ylabel('Probability')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "DfAGt6-rFV71",
        "outputId": "c3b1b3c8-3bee-4ad7-e57b-faacb6bc79e9"
      },
      "execution_count": null,
      "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": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "SUCljBxiFbM1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"/content/demodt.txt\")"
      ],
      "metadata": {
        "id": "xiYhP8zqFkFy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 864
        },
        "id": "ejmuk8b_GMbr",
        "outputId": "52dd2df2-03a4-4ec2-9f48-74d5bd9776fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   State  Literacy  Cleanliness  Crime_Rate  Good\n",
              "0      A        92           90          54     0\n",
              "1      B        56           67          50     1\n",
              "2      C        78           85          62     0\n",
              "3      D        63           72          48     1\n",
              "4      E        85           79          55     0\n",
              "5      F        71           68          58     0\n",
              "6      G        80           83          51     0\n",
              "7      H        67           74          47     1\n",
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 26,\n  \"fields\": [\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 26,\n        \"samples\": [\n          \"I\",\n          \"Q\",\n          \"A\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Literacy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 55,\n        \"max\": 94,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          89,\n          55,\n          92\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cleanliness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 63,\n        \"max\": 92,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          88,\n          66,\n          90\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Crime_Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 41,\n        \"max\": 63,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          54,\n          52,\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Good\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "DT=DecisionTreeClassifier()"
      ],
      "metadata": {
        "id": "Z7Bv79RWGRsw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "target=df.Good\n",
        "print(target)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9raoQWNLOmnz",
        "outputId": "3e67358d-fa34-4d2f-925d-2ba05fd4a382"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0     0\n",
            "1     1\n",
            "2     0\n",
            "3     1\n",
            "4     0\n",
            "5     0\n",
            "6     0\n",
            "7     1\n",
            "8     0\n",
            "9     1\n",
            "10    0\n",
            "11    0\n",
            "12    1\n",
            "13    0\n",
            "14    1\n",
            "15    0\n",
            "16    0\n",
            "17    0\n",
            "18    1\n",
            "19    1\n",
            "20    0\n",
            "21    0\n",
            "22    1\n",
            "23    0\n",
            "24    1\n",
            "25    1\n",
            "Name: Good, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features=['Literacy','Cleanliness','Crime_Rate']\n",
        "feature=df[features]\n",
        "print(feature)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4M2UE3JvOrEs",
        "outputId": "0881063d-9f99-4f60-e9a7-fe276cb24402"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    Literacy  Cleanliness  Crime_Rate\n",
            "0         92           90          54\n",
            "1         56           67          50\n",
            "2         78           85          62\n",
            "3         63           72          48\n",
            "4         85           79          55\n",
            "5         71           68          58\n",
            "6         80           83          51\n",
            "7         67           74          47\n",
            "8         89           88          53\n",
            "9         58           65          49\n",
            "10        82           81          60\n",
            "11        75           78          57\n",
            "12        69           70          46\n",
            "13        87           86          52\n",
            "14        61           63          45\n",
            "15        93           91          56\n",
            "16        55           66          61\n",
            "17        76           77          59\n",
            "18        84           82          44\n",
            "19        70           69          50\n",
            "20        94           92          57\n",
            "21        59           64          52\n",
            "22        83           80          43\n",
            "23        74           76          63\n",
            "24        68           73          41\n",
            "25        88           84          47\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DT.fit(feature,target)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "UaOG2KDXOwz3",
        "outputId": "a4e5bccf-1b01-4b9e-a745-3b407a57a0b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred=DT.predict([[90,90,45]])\n",
        "print(pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BOMWk-5YO1XS",
        "outputId": "7acc7db8-30d2-461b-82e3-4622d1ffee6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred=DT.predict([[90,90,51]])\n",
        "print(pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v2sMEImkO62U",
        "outputId": "a7def94e-a597-48f9-81d5-0a87e6aebd47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred=DT.predict([[90,90,50]])\n",
        "print(pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bqZcFQaKPAw5",
        "outputId": "97c3d005-eac7-4cc0-a8d0-70eb9ccc9ed7"
      },
      "execution_count": null,
      "outputs": [
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred=DT.predict([[90,90,60]])\n",
        "print(pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rp7-TraqPFm6",
        "outputId": "7750f5b2-f3a7-4a72-9292-3ee44d5a9473"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if pred==[0]:\n",
        "  print(\"Not Good\")\n",
        "else:\n",
        "  print(\"Good\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O0LDknYnPJ61",
        "outputId": "29214f3c-a811-4b3a-ef6c-d116d1624992"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Not Good\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a=int(input(\"Enter Literacy rate\"))\n",
        "b=int(input(\"Enter cleanliness\"))\n",
        "c=int(input(\"Enter crime rate\"))\n",
        "pre=DT.predict([[a,b,c]])\n",
        "if pred==[0]:\n",
        "  print(\"Not Good\")\n",
        "else:\n",
        "  print(\"Good\")"
      ],
      "metadata": {
        "id": "VT7RwGg8PNqK"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}