Datasets
Models
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"source": [
"import pandas as pd\n",
"df = pd.read_csv('/content/Iris.csv')"
]
},
{
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"source": [
"import warnings\n",
"warnings.simplefilter(action='ignore', category=FutureWarning)"
],
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"id": "O71P1hzlMcYi"
},
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"outputs": []
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"df.head()\n"
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" Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species\n",
"0 1 5.1 3.5 1.4 0.2 Iris-setosa\n",
"1 2 4.9 3.0 1.4 0.2 Iris-setosa\n",
"2 3 4.7 3.2 1.3 0.2 Iris-setosa\n",
"3 4 4.6 3.1 1.5 0.2 Iris-setosa\n",
"4 5 5.0 3.6 1.4 0.2 Iris-setosa"
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"metadata": {},
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"execution_count": 33
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"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 150 entries, 0 to 149\n",
"Data columns (total 6 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Id 150 non-null int64 \n",
" 1 SepalLengthCm 150 non-null float64\n",
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" 4 PetalWidthCm 150 non-null float64\n",
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"memory usage: 7.2+ KB\n"
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"Id 0\n",
"SepalLengthCm 0\n",
"SepalWidthCm 0\n",
"PetalLengthCm 0\n",
"PetalWidthCm 0\n",
"Species 0\n",
"dtype: int64"
]
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"metadata": {},
"execution_count": 35
}
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"sns.boxplot(df['SepalLengthCm'])\n"
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"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 428
},
"id": "3eEOMf88NXeb",
"outputId": "8bbec8f5-a78a-4dfd-b2a8-2a68a6c31735"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<Axes: ylabel='SepalLengthCm'>"
]
},
"metadata": {},
"execution_count": 37
},
{
"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": [
"sns.boxplot(df['SepalWidthCm'])\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 431
},
"id": "VOLM27GUNag5",
"outputId": "d6a77fa2-f8e2-4bf4-afa0-4928d33739a6"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<Axes: ylabel='SepalWidthCm'>"
]
},
"metadata": {},
"execution_count": 38
},
{
"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": [
"q1 = df['SepalWidthCm'].quantile(0.25)\n",
"print(q1)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "35aGeEPMRqia",
"outputId": "a0b8bf3f-9331-4f5c-ec48-66b0d3bd07d2"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2.8\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"q3 = df['SepalWidthCm'].quantile(0.75)\n",
"q3\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xMlV3O4hRtbQ",
"outputId": "1d9de9e8-0a02-4ec8-aef2-d33cc7be2290"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"3.3"
]
},
"metadata": {},
"execution_count": 40
}
]
},
{
"cell_type": "code",
"source": [
"IQR = q3-q1\n",
"IQR"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FGryZam7Rvhl",
"outputId": "78ad6398-ae90-423c-acc6-c2067efb65c2"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.5"
]
},
"metadata": {},
"execution_count": 41
}
]
},
{
"cell_type": "code",
"source": [
"lowerBound = q1 -(1.5*IQR)\n",
"print(lowerBound)\n",
"upperBound = q3 + (1.5*IQR)\n",
"print(upperBound)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "i5IqloiSRyK7",
"outputId": "d65c6f33-0593-4398-f554-cdb9a96fdc1c"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2.05\n",
"4.05\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"sns.boxplot(df['PetalLengthCm'])\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 428
},
"id": "kZfuU6FER1VI",
"outputId": "efe0c5c4-a4f3-4a77-8f4b-2a39deeb1eb8"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<Axes: ylabel='PetalLengthCm'>"
]
},
"metadata": {},
"execution_count": 43
},
{
"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": [
"sns.boxplot(df['PetalWidthCm'])\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 429
},
"id": "N3q6sCWtR_F_",
"outputId": "5b3a4705-d42c-4e19-d802-6764f3c3a527"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<Axes: ylabel='PetalWidthCm'>"
]
},
"metadata": {},
"execution_count": 44
},
{
"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": [
"from sklearn.preprocessing import LabelEncoder\n",
"le = LabelEncoder()\n",
"df['Species']=le.fit_transform(df[['Species']])\n",
"sns.boxplot(df['Species'])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 483
},
"id": "CRqR9_HeSB2T",
"outputId": "5f7663da-5d27-4529-a2c7-cc9d67567d3d"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_label.py:116: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True)\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<Axes: ylabel='Species'>"
]
},
"metadata": {},
"execution_count": 45
},
{
"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": [
"from sklearn.preprocessing import StandardScaler\n",
"Scaler = StandardScaler()\n",
"df['Id']=Scaler.fit_transform(df[['Id']])\n",
"df['SepalLengthCm']=Scaler.fit_transform(df[['SepalLengthCm']])\n",
"df['SepalWidthCm']=Scaler.fit_transform(df[['SepalWidthCm']])\n",
"df['PetalLengthCm']=Scaler.fit_transform(df[['PetalLengthCm']])\n",
"df['PetalWidthCm']=Scaler.fit_transform(df[['PetalWidthCm']])\n"
],
"metadata": {
"id": "XGSZdbjnSFBU"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df.head()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "E4RZnLT9SHky",
"outputId": "ee93906b-6843-4241-d5ff-1f5cd394d4d1"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species\n",
"0 -1.720542 -0.900681 1.032057 -1.341272 -1.312977 0\n",
"1 -1.697448 -1.143017 -0.124958 -1.341272 -1.312977 0\n",
"2 -1.674353 -1.385353 0.337848 -1.398138 -1.312977 0\n",
"3 -1.651258 -1.506521 0.106445 -1.284407 -1.312977 0\n",
"4 -1.628164 -1.021849 1.263460 -1.341272 -1.312977 0"
],
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" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-aa371034-0785-42ca-8e74-9e024c21c399 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-aa371034-0785-42ca-8e74-9e024c21c399');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
"<div id=\"df-2022d00d-9d8d-4838-bedb-3c03c259b22f\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2022d00d-9d8d-4838-bedb-3c03c259b22f')\"\n",
" title=\"Suggest charts\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <g>\n",
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-2022d00d-9d8d-4838-bedb-3c03c259b22f button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "df",
"summary": "{\n \"name\": \"df\",\n \"rows\": 150,\n \"fields\": [\n {\n \"column\": \"Id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0033500931359767,\n \"min\": -1.7205420368774056,\n \"max\": 1.7205420368774056,\n \"num_unique_values\": 150,\n \"samples\": [\n -0.03464178597739743,\n -1.3048406051486365,\n 1.0046117933445253\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"SepalLengthCm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0033500931359767,\n \"min\": -1.870024133847019,\n \"max\": 2.4920192021244283,\n \"num_unique_values\": 35,\n \"samples\": [\n 0.432165404582356,\n -1.6276883929597161,\n -0.29484181807955234\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"SepalWidthCm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0033500931359767,\n \"min\": -2.438987252491841,\n \"max\": 3.1146839106774356,\n \"num_unique_values\": 23,\n \"samples\": [\n -1.7447783570956819,\n 2.1890720501492225,\n 1.0320572244889565\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PetalLengthCm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0033500931359765,\n \"min\": -1.5687352207168408,\n \"max\": 1.7863413146490472,\n \"num_unique_values\": 43,\n \"samples\": [\n 1.6726099066705424,\n 0.02350449098222449,\n -0.03336121300702764\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PetalWidthCm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0033500931359767,\n \"min\": -1.4444496972795189,\n \"max\": 1.7109015831854495,\n \"num_unique_values\": 22,\n \"samples\": [\n -1.3129767272601454,\n 0.001752972933591456,\n 0.13322594295296525\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Species\",\n \"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": 47
}
]
},
{
"cell_type": "code",
"source": [
"x= df.iloc[:,0:-1]\n",
"x\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 423
},
"id": "12te3PXRSKzJ",
"outputId": "3b4e22f4-72ec-4303-bb09-a4f367496301"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm\n",
"0 -1.720542 -0.900681 1.032057 -1.341272 -1.312977\n",
"1 -1.697448 -1.143017 -0.124958 -1.341272 -1.312977\n",
"2 -1.674353 -1.385353 0.337848 -1.398138 -1.312977\n",
"3 -1.651258 -1.506521 0.106445 -1.284407 -1.312977\n",
"4 -1.628164 -1.021849 1.263460 -1.341272 -1.312977\n",
".. ... ... ... ... ...\n",
"145 1.628164 1.038005 -0.124958 0.819624 1.447956\n",
"146 1.651258 0.553333 -1.281972 0.705893 0.922064\n",
"147 1.674353 0.795669 -0.124958 0.819624 1.053537\n",
"148 1.697448 0.432165 0.800654 0.933356 1.447956\n",
"149 1.720542 0.068662 -0.124958 0.762759 0.790591\n",
"\n",
"[150 rows x 5 columns]"
],
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Id</th>\n",
" <th>SepalLengthCm</th>\n",
" <th>SepalWidthCm</th>\n",
" <th>PetalLengthCm</th>\n",
" <th>PetalWidthCm</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-1.720542</td>\n",
" <td>-0.900681</td>\n",
" <td>1.032057</td>\n",
" <td>-1.341272</td>\n",
" <td>-1.312977</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-1.697448</td>\n",
" <td>-1.143017</td>\n",
" <td>-0.124958</td>\n",
" <td>-1.341272</td>\n",
" <td>-1.312977</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-1.674353</td>\n",
" <td>-1.385353</td>\n",
" <td>0.337848</td>\n",
" <td>-1.398138</td>\n",
" <td>-1.312977</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-1.651258</td>\n",
" <td>-1.506521</td>\n",
" <td>0.106445</td>\n",
" <td>-1.284407</td>\n",
" <td>-1.312977</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-1.628164</td>\n",
" <td>-1.021849</td>\n",
" <td>1.263460</td>\n",
" <td>-1.341272</td>\n",
" <td>-1.312977</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
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" <td>...</td>\n",
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" <tr>\n",
" <th>145</th>\n",
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" <td>1.038005</td>\n",
" <td>-0.124958</td>\n",
" <td>0.819624</td>\n",
" <td>1.447956</td>\n",
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" <tr>\n",
" <th>146</th>\n",
" <td>1.651258</td>\n",
" <td>0.553333</td>\n",
" <td>-1.281972</td>\n",
" <td>0.705893</td>\n",
" <td>0.922064</td>\n",
" </tr>\n",
" <tr>\n",
" <th>147</th>\n",
" <td>1.674353</td>\n",
" <td>0.795669</td>\n",
" <td>-0.124958</td>\n",
" <td>0.819624</td>\n",
" <td>1.053537</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>1.697448</td>\n",
" <td>0.432165</td>\n",
" <td>0.800654</td>\n",
" <td>0.933356</td>\n",
" <td>1.447956</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149</th>\n",
" <td>1.720542</td>\n",
" <td>0.068662</td>\n",
" <td>-0.124958</td>\n",
" <td>0.762759</td>\n",
" <td>0.790591</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>150 rows × 5 columns</p>\n",
"</div>\n",
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" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
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"\n",
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" margin-bottom: 4px;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
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" fill: #D2E3FC;\n",
" }\n",
"\n",
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" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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"\n",
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" const element = document.querySelector('#df-0f5d55d2-8d7d-443d-b2d6-ecd7bc8dab29');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
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" title=\"Suggest charts\"\n",
" style=\"display:none;\">\n",
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"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
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" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
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"metadata": {},
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{
"cell_type": "code",
"source": [
"y = df.iloc[:,-1]\n",
"y"
],
"metadata": {
"colab": {
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},
"id": "ks3ppPf9SNWu",
"outputId": "8f2afa6e-20a4-4a45-b5ed-eced34d4b7bb"
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},
"metadata": {},
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]
},
{
"cell_type": "code",
"source": [
"from sklearn.model_selection import train_test_split\n",
"x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.2, random_state=42)"
],
"metadata": {
"id": "6lahNVxMSQLu"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"x_train.shape"
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"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "doAXLqATSSpt",
"outputId": "e6f0bf92-eaed-4ee2-f706-3cf6df5b5126"
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"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(120, 5)"
]
},
"metadata": {},
"execution_count": 51
}
]
},
{
"cell_type": "code",
"source": [
"x_test.shape"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
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"id": "6aC4YClQSV9y",
"outputId": "4a991db0-4112-47aa-b28f-d23abd8fbcc3"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(30, 5)"
]
},
"metadata": {},
"execution_count": 52
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.tree import DecisionTreeClassifier\n",
"dtc=DecisionTreeClassifier(criterion='entropy', max_depth=3)\n",
"dtc.fit(x_train,y_train)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 74
},
"id": "gGjKZXuESYVt",
"outputId": "22b324ce-8f1f-41c5-cfde-1f88f19405a3"
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"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DecisionTreeClassifier(criterion='entropy', max_depth=3)"
],
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]
},
"metadata": {},
"execution_count": 53
}
]
},
{
"cell_type": "code",
"source": [
"y_pred = dtc.predict(x_test)\n",
"y_pred\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Y_M2HmyTSae7",
"outputId": "4f539e7f-5173-42ff-f437-640ca46c95f2"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([1, 0, 2, 1, 1, 0, 1, 2, 1, 1, 2, 0, 0, 0, 0, 1, 2, 1, 1, 2, 0, 2,\n",
" 0, 2, 2, 2, 2, 2, 0, 0])"
]
},
"metadata": {},
"execution_count": 54
}
]
},
{
"cell_type": "code",
"source": [
"dtc.predict([[151,0.5,1,2,1.5]])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kaJivozYSdBD",
"outputId": "fc4989d3-6a63-483b-bfbf-4b9080f952f2"
},
"execution_count": null,
"outputs": [
{
"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"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([2])"
]
},
"metadata": {},
"execution_count": 55
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.metrics import accuracy_score\n",
"accuracy_score(y_pred,y_test)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "sX6HdnKOSgFG",
"outputId": "eb8b659e-ea11-4241-ef54-ab325775d661"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1.0"
]
},
"metadata": {},
"execution_count": 56
}
]
},
{
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"from sklearn import tree\n",
"plt.figure(figsize=(10,10))\n",
"tree.plot_tree(dtc.fit(x_train,y_train))\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 807
},
"id": "vxQ-876oSiRP",
"outputId": "194591f3-1d67-4035-d09f-6640f30272e0"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x1000 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "3_8uB_qOVKyQ"
},
"execution_count": null,
"outputs": []
}
]
}