{
"cells": [
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"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"import plotly.express as px\n",
"import plotly.graph_objects as go\n",
"from sklearn.ensemble import RandomForestRegressor\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.feature_selection import mutual_info_regression\n",
"\n",
"\n",
"\n",
"pd.set_option('display.max_columns', None)\n",
"pd.set_option('display.max_rows', None)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"ad_test_research = pd.read_excel(\"data/Datathon@MetuStatClub Final.xlsx\", sheet_name=\"Reklam Test Araştırması\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# clean the data\n",
"ad_test_research = ad_test_research.replace(r'^[xX]+$', np.nan, regex=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"cat_features = [\n",
" \"Product status\",\n",
" \"Ad format\",\n",
"]\n",
"\n",
"drop_features = [ # all of these features has only NaN values\n",
" \"BOX\",\n",
" \"BOX.1\",\n",
" \"Portrays Top\",\n",
" \"Portrays Top2\",\n",
" \"Will win awards Top\",\n",
" \"Will win awards Top2\",\n",
" \"Is interesting Top\",\n",
" \"Is interesting Top2\",\n",
" \"Is offensive Top\",\n",
" \"Is offensive Top2\",\n",
" \"Is silly Top\",\n",
" \"Is silly Top2\",\n",
" \"Is humourus Top\",\n",
" \"Is humourus Top2\",\n",
" \"Is clever Top\",\n",
" \"Is clever Top2\",\n",
" \"Is lively Top\",\n",
" \"Is lively Top2\",\n",
" \"Is imaginative Top\",\n",
" \"Is imaginative Top2\",\n",
" \"Is convincing Top\",\n",
" \"Is convincing Top2\",\n",
" \"P_Relevance Top\",\n",
" \"P_Relevance Top2\",\n",
" \"P_Differentiation Top\",\n",
" \"P_Differentiation Top2\",\n",
" \"P_Popularity Top\",\n",
" \"P_Popularity Top2\",\n",
" \"P_Familiarity Top\",\n",
" \"P_Familiarity Top2\",\n",
" \"P_Quality Top\",\n",
" \"P_Quality Top2\",\n",
" \"P_Commitment Top (Only Brand for me)\",\n",
" \"P_Commitment Top2 (Only Brand for me)\",\n",
" \"Is warm Top2\", # these two are not entirely null but mostly null\n",
" \"Is warm Top\",\n",
"]\n",
"\n",
"df = pd.get_dummies(data=ad_test_research, columns=cat_features, dtype=np.int8)\n",
"df = df.drop(drop_features, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2 rows discarded that had 0.576271186440678% null values\n"
]
}
],
"source": [
"rows_to_discard = df.isna().sum(axis=1).sort_values(ascending=False)[:2]\n",
"print(f\"{len(rows_to_discard)} rows discarded that had {list(rows_to_discard)[0] / df.shape[1]}% null values\")\n",
"df = df.drop(rows_to_discard.index.tolist(), axis=0)\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of empty values in each column:\n"
]
},
{
"data": {
"text/plain": [
"Appetizing Top 88\n",
"Appetizing Top2 88\n",
"Chance_Opinion Top2 14\n",
"Chance_Opinion Top 14\n",
"Mesaj Hitap (Top2Box) 13\n",
"Mesaj İletimi (Top2Box) 12\n",
"Mesaj İnandırıcılığı (Top2Box) 12\n",
"Told important Top2 2\n",
"Told important Top 2\n",
"Likeability Bottom 2\n",
"dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(\"Number of empty values in each column:\")\n",
"df.isna().sum(axis=0).sort_values(ascending=False)[:10]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# fill the null values with mean\n",
"df = df.fillna(df.mean())"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"df_top = df.filter(regex='Top$', axis=1)\n",
"df_top2 = df.filter(regex='Top2$', axis=1)\n",
"\n",
"df_top1 = df_top.rename(columns=lambda x: x.replace(' Top', ''))\n",
"df_top2 = df_top2.rename(columns=lambda x: x.replace(' Top2', ''))\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((153, 59), (153, 21), (153, 21))"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape, df_top1.shape, df_top2.shape"
]
},
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"attachments": {},
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"metadata": {},
"source": [
"## Correlation "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: kaleido in ./.venv/lib/python3.11/site-packages (0.2.1)\n"
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" fig = px.bar(y=correlation.index, x=correlation.values, orientation=\"h\", title=\"Correlation with Notice & remember\", labels={\"x\": \"Correlation\", \"y\": \"Features\"}, height=800, width=1000)\n",
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"source": [
"# lets draw a graph out of mutual information \n",
"\n",
"def plot_mutual_information(df):\n",
" X = df.drop([\"Notice & remember\"], axis=1)\n",
" y = df[\"Notice & remember\"]\n",
"\n",
" X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
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" mi = mutual_info_regression(X_train, y_train)\n",
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" results = mi.sort_values(ascending=False)[:10]\n",
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" fig = px.bar(y=results.index, x=results.values, orientation=\"h\", title=\"Mutual Information\", labels={\"x\": \"Mutual Information\", \"y\": \"Features\"}, height=800, width=1000)\n",
" fig.show()\n",
" # save the plot\n",
" fig.write_image(\"plots/mutual_information.png\")\n",
" \n",
"\n",
"plot_mutual_information(df_top1)"
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"Mutual information (MI) between two random variables is a non-negative value, which measures the dependency between the variables. It is equal to zero if and only if two random variables are independent, and higher values mean higher dependency.\n",
"\n",
"The difference between correlation and mutual information is that correlation measures linear relationship between two continuous variables. Mutual information measures the information that X and Y share: it measures how much knowing one of these variables reduces uncertainty about the other.\n",
"\n",
"But in a semantic way, correlation is a measure of how much two variables change together, while mutual information is a measure of how much knowing one variable tells you about the other.\n",
"\n"
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