{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Hollywood_clustering.ipynb",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Q-Zlfgg_2c7L",
"outputId": "da909070-acbd-475c-a5ae-27834f0c0c1c"
},
"source": [
"!which python3"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/bin/python3\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "1GBbUvNl2tGM"
},
"source": [
"import numpy as np\r\n",
"from sklearn.cluster import KMeans \r\n",
"import pandas as pd\r\n",
"import matplotlib.pyplot as plt\r\n",
"%matplotlib inline \r\n",
"import seaborn as sns"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "hN-RNN4u2zEV"
},
"source": [
"df=pd.read_csv('/content/ActorsDataset.csv', index_col=0)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 223
},
"id": "gW1gyyRb3D4m",
"outputId": "0b776dfe-5a10-4bfd-a78a-e2199317e15c"
},
"source": [
"df.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Gender | \n",
" Age | \n",
" Net Worth, Millions $ | \n",
" Number of children | \n",
" Name | \n",
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\n",
" \n",
" ID | \n",
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" | \n",
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" 78 | \n",
" 300 | \n",
" 5 | \n",
" Harrison Ford | \n",
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\n",
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" 47 | \n",
" 50 | \n",
" 2 | \n",
" Neil Patrick Harris | \n",
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" 72 | \n",
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" 41 | \n",
" 10 | \n",
" 2 | \n",
" Oscar Isaac | \n",
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\n",
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" 5 | \n",
" Male | \n",
" 47 | \n",
" 7 | \n",
" 2 | \n",
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"text/plain": [
" Gender Age Net Worth, Millions $ Number of children Name\n",
"ID \n",
"1 Male 78 300 5 Harrison Ford\n",
"2 Male 47 50 2 Neil Patrick Harris\n",
"3 Male 72 16 2 Jeremy Irons\n",
"4 Male 41 10 2 Oscar Isaac\n",
"5 Male 47 7 2 Patrick Wilson"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 428
},
"id": "ukKILJ9kj97Z",
"outputId": "30fcf02b-21ee-44d0-84e6-659f6ad32ee8"
},
"source": [
"df"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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\n",
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"text/plain": [
" Gender Age ... Number of children Name\n",
"ID ... \n",
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"3 Male 72 ... 2 Jeremy Irons\n",
"4 Male 41 ... 2 Oscar Isaac\n",
"5 Male 47 ... 2 Patrick Wilson\n",
".. ... ... ... ... ...\n",
"252 Female 85 ... 5 Julie Andrews\n",
"253 Female 24 ... 0 Hailee Steinfeld\n",
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"\n",
"[256 rows x 5 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vFRu1zta3Ikb",
"outputId": "8620270b-f0c8-40b7-a12e-2b6401941f39"
},
"source": [
"df.info()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"\n",
"Int64Index: 256 entries, 1 to 256\n",
"Data columns (total 5 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Gender 256 non-null object\n",
" 1 Age 256 non-null int64 \n",
" 2 Net Worth, Millions $ 256 non-null int64 \n",
" 3 Number of children 256 non-null int64 \n",
" 4 Name 256 non-null object\n",
"dtypes: int64(3), object(2)\n",
"memory usage: 12.0+ KB\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
},
"id": "veuZ12ss3LbL",
"outputId": "cb8ad357-cb58-426e-b0a9-da012a84b593"
},
"source": [
"df.describe()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Age | \n",
" Net Worth, Millions $ | \n",
" Number of children | \n",
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\n",
" \n",
" \n",
" \n",
" count | \n",
" 256.000000 | \n",
" 256.000000 | \n",
" 256.000000 | \n",
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\n",
" \n",
" mean | \n",
" 51.433594 | \n",
" 79.718750 | \n",
" 1.792969 | \n",
"
\n",
" \n",
" std | \n",
" 15.213625 | \n",
" 97.725942 | \n",
" 1.531406 | \n",
"
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" \n",
" min | \n",
" 20.000000 | \n",
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" 50% | \n",
" 50.000000 | \n",
" 40.000000 | \n",
" 2.000000 | \n",
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" 75% | \n",
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" 100.000000 | \n",
" 3.000000 | \n",
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\n",
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"text/plain": [
" Age Net Worth, Millions $ Number of children\n",
"count 256.000000 256.000000 256.000000\n",
"mean 51.433594 79.718750 1.792969\n",
"std 15.213625 97.725942 1.531406\n",
"min 20.000000 3.000000 0.000000\n",
"25% 40.000000 16.000000 0.750000\n",
"50% 50.000000 40.000000 2.000000\n",
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},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 528
},
"id": "Qysbd6_I3i2f",
"outputId": "4d413eb5-dfab-48c9-9c6b-47bdc8402ead"
},
"source": [
"sns.set_style('whitegrid')\r\n",
"sns.lmplot('Age','Net Worth, Millions $',data=df, hue='Gender',\r\n",
" palette='coolwarm',height=6,aspect=1,fit_reg=False)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
" FutureWarning\n"
],
"name": "stderr"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {
"tags": []
},
"execution_count": 8
},
{
"output_type": "display_data",
"data": {
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\n",
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