{ "cells": [ { "cell_type": "code", "execution_count": 2, "id": "bf29b5ec", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agebpsgalsurbcpcpccbabgr...pcvwcrchtndmcadappetpeaneclassification
048.080.01.0201.00.0NaNnormalnotpresentnotpresent121.0...4478005.2yesyesnogoodnonockd
17.050.01.0204.00.0NaNnormalnotpresentnotpresentNaN...386000NaNnononogoodnonockd
262.080.01.0102.03.0normalnormalnotpresentnotpresent423.0...317500NaNnoyesnopoornoyesckd
348.070.01.0054.00.0normalabnormalpresentnotpresent117.0...3267003.9yesnonopooryesyesckd
451.080.01.0102.00.0normalnormalnotpresentnotpresent106.0...3573004.6nononogoodnonockd
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5 rows × 25 columns

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" ], "text/plain": [ " age bp sg al su rbc pc pcc ba \\\n", "0 48.0 80.0 1.020 1.0 0.0 NaN normal notpresent notpresent \n", "1 7.0 50.0 1.020 4.0 0.0 NaN normal notpresent notpresent \n", "2 62.0 80.0 1.010 2.0 3.0 normal normal notpresent notpresent \n", "3 48.0 70.0 1.005 4.0 0.0 normal abnormal present notpresent \n", "4 51.0 80.0 1.010 2.0 0.0 normal normal notpresent notpresent \n", "\n", " bgr ... pcv wc rc htn dm cad appet pe ane classification \n", "0 121.0 ... 44 7800 5.2 yes yes no good no no ckd \n", "1 NaN ... 38 6000 NaN no no no good no no ckd \n", "2 423.0 ... 31 7500 NaN no yes no poor no yes ckd \n", "3 117.0 ... 32 6700 3.9 yes no no poor yes yes ckd \n", "4 106.0 ... 35 7300 4.6 no no no good no no ckd \n", "\n", "[5 rows x 25 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from sklearn.preprocessing import LabelEncoder\n", "\n", "df = pd.read_csv('Desktop/kidney_disease.csv')\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 3, "id": "cfd90767", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Columns dropped: ['rbc', 'rc']\n", "Remaining columns: Index(['age', 'bp', 'sg', 'al', 'su', 'pc', 'pcc', 'ba', 'bgr', 'bu', 'sc',\n", " 'sod', 'pot', 'hemo', 'pcv', 'wc', 'htn', 'dm', 'cad', 'appet', 'pe',\n", " 'ane', 'classification'],\n", " dtype='object')\n" ] } ], "source": [ "missing_percentage = df.isnull().mean() * 100\n", "columns_to_drop = missing_percentage[missing_percentage > 30].index\n", "df = df.drop(columns=columns_to_drop)\n", "\n", "print(f\"Columns dropped: {list(columns_to_drop)}\")\n", "print(f\"Remaining columns: {df.columns}\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "3cb213a7", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agebpsgalsupcpccbabgrbu...hemopcvwchtndmcadappetpeaneclassification
048.080.01.0201.00.0211121.036.0...15.432722520110
17.050.01.0204.00.0211NaN18.0...11.326561420110
262.080.01.0102.03.0211423.053.0...9.619701522120
348.070.01.0054.00.0021117.056.0...11.220622422220
451.080.01.0102.00.0211106.026.0...11.623681420110
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5 rows × 23 columns

\n", "
" ], "text/plain": [ " age bp sg al su pc pcc ba bgr bu ... hemo pcv wc \\\n", "0 48.0 80.0 1.020 1.0 0.0 2 1 1 121.0 36.0 ... 15.4 32 72 \n", "1 7.0 50.0 1.020 4.0 0.0 2 1 1 NaN 18.0 ... 11.3 26 56 \n", "2 62.0 80.0 1.010 2.0 3.0 2 1 1 423.0 53.0 ... 9.6 19 70 \n", "3 48.0 70.0 1.005 4.0 0.0 0 2 1 117.0 56.0 ... 11.2 20 62 \n", "4 51.0 80.0 1.010 2.0 0.0 2 1 1 106.0 26.0 ... 11.6 23 68 \n", "\n", " htn dm cad appet pe ane classification \n", "0 2 5 2 0 1 1 0 \n", "1 1 4 2 0 1 1 0 \n", "2 1 5 2 2 1 2 0 \n", "3 2 4 2 2 2 2 0 \n", "4 1 4 2 0 1 1 0 \n", "\n", "[5 rows x 23 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "label_encoder = LabelEncoder()\n", "for column in df.select_dtypes(include=['object']).columns:\n", " df[column] = label_encoder.fit_transform(df[column].astype(str))\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 5, "id": "87e3fe25", "metadata": {}, "outputs": [], "source": [ "for column in df.select_dtypes(include=['float64', 'int64']).columns:\n", " df[column] = df[column].fillna(df[column].mean())" ] }, { "cell_type": "code", "execution_count": 6, "id": "95e4788c", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '1.092964824120603' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '1.0452261306532664' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '4.276381909547739' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '2.07035175879397' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '0.4045226130653266' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '1.1884422110552764' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '1.1457286432160805' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", " column[outliers] = column.mean()\n", "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\1453198383.py:8: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " column[outliers] = column.mean()\n" ] } ], "source": [ "def replace_outliers_with_mean(column):\n", " Q1 = column.quantile(0.25)\n", " Q3 = column.quantile(0.75)\n", " IQR = Q3 - Q1\n", " lower_bound = Q1 - 1.5 * IQR\n", " upper_bound = Q3 + 1.5 * IQR\n", " outliers = (column < lower_bound) | (column > upper_bound)\n", " column[outliers] = column.mean()\n", " return column\n", "\n", "for col in df.select_dtypes(include=[np.number]).columns:\n", " df[col] = replace_outliers_with_mean(df[col])" ] }, { "cell_type": "code", "execution_count": 7, "id": "f0fc5845", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agebpsgalsupcpccbabgrbu...hemopcvwchtndmcadappetpeaneclassification
048.00000080.0000001.0200001.00.00000021.0000001.0121.00000036.0...15.4327225.02.00.0000001.0000001.0000000
151.39588776.5025911.0200004.00.00000021.0000001.0147.94350318.0...11.3265614.02.00.0000001.0000001.0000000
262.00000080.0000001.0100002.00.45142921.0000001.0147.94350353.0...9.6197015.02.00.4045231.0000001.1457290
348.00000070.0000001.0174294.00.00000001.0929651.0117.00000056.0...11.2206224.02.00.4045231.1884421.1457290
451.00000080.0000001.0100002.00.00000021.0000001.0106.00000026.0...11.6236814.02.00.0000001.0000001.0000000
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5 rows × 23 columns

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" ], "text/plain": [ " age bp sg al su pc pcc ba \\\n", "0 48.000000 80.000000 1.020000 1.0 0.000000 2 1.000000 1.0 \n", "1 51.395887 76.502591 1.020000 4.0 0.000000 2 1.000000 1.0 \n", "2 62.000000 80.000000 1.010000 2.0 0.451429 2 1.000000 1.0 \n", "3 48.000000 70.000000 1.017429 4.0 0.000000 0 1.092965 1.0 \n", "4 51.000000 80.000000 1.010000 2.0 0.000000 2 1.000000 1.0 \n", "\n", " bgr bu ... hemo pcv wc htn dm cad appet pe \\\n", "0 121.000000 36.0 ... 15.4 32 72 2 5.0 2.0 0.000000 1.000000 \n", "1 147.943503 18.0 ... 11.3 26 56 1 4.0 2.0 0.000000 1.000000 \n", "2 147.943503 53.0 ... 9.6 19 70 1 5.0 2.0 0.404523 1.000000 \n", "3 117.000000 56.0 ... 11.2 20 62 2 4.0 2.0 0.404523 1.188442 \n", "4 106.000000 26.0 ... 11.6 23 68 1 4.0 2.0 0.000000 1.000000 \n", "\n", " ane classification \n", "0 1.000000 0 \n", "1 1.000000 0 \n", "2 1.145729 0 \n", "3 1.145729 0 \n", "4 1.000000 0 \n", "\n", "[5 rows x 23 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.to_csv('kidney_disease_cleaned.csv', index=False)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 8, "id": "6240b35e", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 6))\n", "sns.histplot(df['age'], bins=15, kde=True, color='blue')\n", "plt.title('Age Distribution')\n", "plt.xlabel('Age')\n", "plt.ylabel('Frequency')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 9, "id": "14c14e81", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\161163265.py:2: FutureWarning: \n", "\n", "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", "\n", " sns.countplot(x='classification', data=df, palette='Set2')\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 6))\n", "sns.countplot(x='classification', data=df, palette='Set2')\n", "plt.title('Classification Count')\n", "plt.xlabel('Classification')\n", "plt.ylabel('Count')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "id": "85489df9", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 6))\n", "sns.scatterplot(x='age', y='hemo', data=df, hue='classification', palette='Set1')\n", "plt.title('Age vs Hemoglobin')\n", "plt.xlabel('Age')\n", "plt.ylabel('Hemoglobin')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "id": "b951312d", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\a1the\\AppData\\Local\\Temp\\ipykernel_23260\\4071781715.py:2: FutureWarning: \n", "\n", "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", "\n", " sns.boxplot(x='classification', y='hemo', data=df, palette='Set1')\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 6))\n", "sns.boxplot(x='classification', y='hemo', data=df, palette='Set1')\n", "plt.title('Hemoglobin Levels by Classification')\n", "plt.xlabel('Classification')\n", "plt.ylabel('Hemoglobin')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 12, "id": "2376964b-ec67-468e-a0e8-afb1a3b8594e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Cleaned dataset saved as: kidney_disease_cleaned.csv\n" ] } ], "source": [ "output_file = 'kidney_disease_cleaned.csv'\n", "df.to_csv(output_file, index=False)\n", "print(f\" Cleaned dataset saved as: {output_file}\")" ] }, { "cell_type": "code", "execution_count": null, "id": "b0c95df4-c4c8-45d4-bc5b-85cd553fb660", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 5 }