Casey-MLProject.ipynb

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   "source": [
    "# Using Naive Bayes to predict 2017 County Data: Casey Surratt\n",
    "\n",
    "This document covers all of my code which was entirely hand written except for the plotting code which I found and edited."
   ]
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          "geo": {
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           "landcolor": "#E5ECF6",
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        },
        "title": {
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   "source": [
    "#Casey\n",
    "\n",
    "# Import statements and some very minor data exploration\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import math\n",
    "# sns.set_theme()\n",
    "og_df = pd.read_csv('archive/acs2017_county_data.csv')\n",
    "# df.describe()\n",
    "\n",
    "# plt.hist(og_df['Income'], bins=100)\n",
    "\n",
    "ystr = \"Count\"\n",
    "xstr = \"IncomePerCap\"\n",
    "\n",
    "wfh_v_private = px.histogram(og_df, x=xstr, title=xstr).update_layout(yaxis_title=ystr, xaxis_title=xstr)\n",
    "wfh_v_private\n",
    "# og_df.columns\n",
    "# og_df.head()"
   ]
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   "cell_type": "markdown",
   "id": "487f5f99-1a17-435a-b39a-f7abc43e42e9",
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   "source": [
    "## Data Cleaning\n",
    "My first challenge was to make sure I only had data I wanted to use, so I removed the id column and county because they were unique values which would be useless data. I then combined Men and Women into a Men% column (I left out the women% column because it appeared to be binary in the data and so can be infered from men%). Later I discovered that I also needed to drop the pacific column because it was causing double overflow error because they were all so small.\n",
    "\n",
    "## Data Binning\n",
    "The second thing I had to do was to bin the features. Because Naive Bayes requires keeping track of every feature value's conditional probability, There must be discrete and  overlaping values for each feature. I decided to simply bin every feature by equal frequency (so 33% of the data in each), evenly dividing every feature's values into 3 parts: Low, Med, High.\n",
    "I experiment later with binning the label into different sizes but the code which does it is in here. (On a side note, I'm also dropping things directly related to the thing I'm checking so if I'm looking at income, I'm dropping IncomePerCap etc.)"
   ]
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   "cell_type": "code",
   "execution_count": 2,
   "id": "d40f85e9-dde8-4e4a-8f0c-8b72296459b4",
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   "source": [
    "# Casey\n",
    "\n",
    "# Removes columns from the dataframe and then bins all of the remaining columns into \"low\" \"Med\" \"High\" divided by even frequency\n",
    "def pruning_and_binning (og_df, labelBins, labels, labelCol):\n",
    "    \n",
    "    \n",
    "    df_pruned = og_df\n",
    "    df_pruned = df_pruned.drop('CountyId', axis=1)\n",
    "    df_pruned = df_pruned.drop('County', axis=1) \n",
    "    \n",
    "    df_pruned['MenPercent'] = round(df_pruned['Men'] / df_pruned['TotalPop'], 2)\n",
    "    # df_pruned['WomenPercent'] = round(df_pruned['Women'] / df_pruned['TotalPop'], 2)\n",
    "    df_pruned = df_pruned.drop('Men', axis=1)\n",
    "    df_pruned = df_pruned.drop('Women', axis=1)\n",
    "    \n",
    "    df_pruned = df_pruned.drop('Pacific', axis=1)\n",
    "\n",
    "    if(labelCol == 'Income'):\n",
    "        df_pruned = df_pruned.drop('IncomePerCap', axis=1)\n",
    "        df_pruned = df_pruned.drop('IncomePerCapErr', axis=1)\n",
    "        df_pruned = df_pruned.drop('IncomeErr', axis=1)\n",
    "    elif(labelCol == 'IncomePerCap'):\n",
    "        df_pruned = df_pruned.drop('Income', axis=1)\n",
    "        df_pruned = df_pruned.drop('IncomePerCapErr', axis=1)\n",
    "        df_pruned = df_pruned.drop('IncomeErr', axis=1)\n",
    "    \n",
    "    \n",
    "    # df_additions.head()\n",
    "    # df_additions.columns\n",
    "\n",
    "    # Bin Everything\n",
    "    df_binned = df_pruned\n",
    "\n",
    "    # For Specific sized bins\n",
    "    df_binned[labelCol] = pd.cut(x = df_pruned[labelCol], bins = labelBins, labels = labels, include_lowest = True)\n",
    "\n",
    "    for i in range(len(df_binned.columns)-1):\n",
    "        colName = df_binned.columns[i+1]\n",
    "        if(colName != labelCol):\n",
    "            feature_labels = ['Low', 'Mid', 'High']\n",
    "            df_binned[colName], cut_bin = pd.qcut(x = df_pruned[colName], q = 3, labels = feature_labels, retbins = True)\n",
    "        # print(colName)\n",
    "        # print(labels)\n",
    "        # print(cut_bin)\n",
    "    \n",
    "    print(\"Pruned & Binned\\n\")\n",
    "    return df_binned\n",
    "    \n",
    "# df_binned.head()"
   ]
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   "cell_type": "markdown",
   "id": "1b9ba44b-05f2-4364-8aa1-c3e954158802",
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   "source": [
    "## Creating a Train and Test/Validation set\n",
    "My next task was to divide the data to be able to check my model. I simply selected some percent of the data as the Train set and left the rest as the \"Test\" set. Since I was using the \"Test\" set for tuning the model I was planning on treating it like a validation set and actually testing on the 2015 dataset (Although It'd have been different it would have been interesting), however, whoever collected the data changed how they were doing it so I ran out of time to adapt it to fit what I'd written already"
   ]
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  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d4e14b0f-7599-435f-927a-cb41574fc3ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Casey\n",
    "\n",
    "# Splitting into a train & test set\n",
    "\n",
    "def get_trainingSet_and_testSet(df_binned, fraction):\n",
    "    # Gets a fraction as a training set\n",
    "    df_training = df_binned.sample(frac=fraction, replace=False, axis=0)\n",
    "    # Takes the rest and returns it as a test set\n",
    "    df_testSet = df_binned[~df_binned.isin(df_training)].dropna(how = 'all')\n",
    "    # Shuffle Test Set\n",
    "    df_testSet = df_testSet.sample(frac=1, replace=False, axis=0)\n",
    "    print(\"Split & Shuffled\\n\")\n",
    "    return df_training, df_testSet\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b28103f-172f-485c-8e7f-6d32f32b7dc8",
   "metadata": {},
   "source": [
    "## Building the model\n",
    "This is where I actually built the model, I input the train set and label column and it outputs two arrays: THe marginal probability of the label values, and a 3d array holding the conditional probability of each feature given the label value. It's mostly just a lot of code that's getting how many times a value shows up with a given label value and normalizes it. It's not the most efficient way to store it but it was how I first approached it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "837ae1f1-f6fd-4c21-926f-82026750e614",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Casey\n",
    "\n",
    "# Building a Baysian model\n",
    "# Outputs two arrays, The first is the marginal probabilities of all the labels, the\n",
    "# second is the conditional probability of every feature given each label\n",
    "\n",
    "def build_naive_bayes_model(df_train, labelCol):\n",
    "    labelCounts = pd.Series(df_train[labelCol].value_counts())\n",
    "    labelMarginals = labelCounts / len(df_train)\n",
    "    labelConditionals = []\n",
    "\n",
    "    for i in range(len(labelCounts)):\n",
    "        labelConditionals.append([])\n",
    "        curLabel = df_train[labelCol].value_counts().index[i]\n",
    "        df_conditional = df_train[df_train[labelCol] == curLabel]\n",
    "\n",
    "        for j in range(len(df_conditional.columns)):\n",
    "            colOfInterest = [df_conditional.columns[j]]\n",
    "\n",
    "            df1 = df_conditional[colOfInterest].value_counts()\n",
    "            df1 = df1.astype(float)\n",
    "            df1 = (df1 + 1) / (labelCounts[i]+1)\n",
    "\n",
    "    #       Check for values not in the sample and set them to 1/labelCounts\n",
    "            for k in range(len(df_train[colOfInterest].value_counts())):\n",
    "                indexVal = df_train[colOfInterest].value_counts().index[k]\n",
    "                if(indexVal not in df1.index):\n",
    "                    newDF = pd.Series([1/(labelCounts[i]+1)], [indexVal])\n",
    "                    df1 = pd.concat([df1, newDF])\n",
    "            labelConditionals[i].append(df1)\n",
    "\n",
    "            \n",
    "    # This line prints the conditionals table for each label, it's cool but got annoying after a lot of runs\n",
    "    # (Note: despite it containing the probability of the label = 1 if it's the label, that's not used in the predictor)\n",
    "    # print(labelConditionals)\n",
    "    \n",
    "    print(\"Model Built\\n\")\n",
    "    \n",
    "    return labelMarginals, labelConditionals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26b71480-5245-4bf6-a60f-b4a8186fa0ce",
   "metadata": {},
   "source": [
    "## Running the Model\n",
    "This is where I actually use the probabilities. This takes the entire test set and probabilities and finds the most probable label for each item. It then records the number it gets right of each type and the total number of each type it predicts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b096749e-a6cf-4a0a-8765-1ba5c0e81273",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Casey\n",
    "\n",
    "# Evaluates the test set with the Bayes Model, outputting the sum of each label's prediction (totalOutput) \n",
    "# and the sum of each correct prediction (correctOutput)\n",
    "\n",
    "def eval_with_bayes_model(df_testSet, labelMarginals, labelConditionals, label, labels):\n",
    "    print(\"Running Inference Task\\n\")\n",
    "\n",
    "    totalOutput = pd.Series([0]*len(labels), labels)\n",
    "    correctOutput = pd.Series([0]*len(labels), labels)\n",
    "    numCorrect = 0\n",
    "\n",
    "    for k in range(len(df_testSet)):\n",
    "        df_row = df_testSet.iloc[k]\n",
    "\n",
    "        labelProbabilities = labelMarginals.copy()\n",
    "\n",
    "        for i in range(len(labelProbabilities)):\n",
    "            for j in range(len(df_row)):\n",
    "                if(df_row.index[j] != label):\n",
    "                    x = labelConditionals[i][j]\n",
    "                    # print(x.loc[df_row[j]][0])\n",
    "                    # print(df_row[j])\n",
    "                    if df_row[j] in x:\n",
    "                        labelProbabilities[i] *= x.loc[df_row[j]][0]\n",
    "\n",
    "\n",
    "\n",
    "        totalOutput.loc[labelProbabilities.idxmax()] += 1\n",
    "        if(labelProbabilities.idxmax() == df_row.loc[label]):\n",
    "            numCorrect += 1\n",
    "            correctOutput.loc[labelProbabilities.idxmax()] += 1\n",
    "\n",
    "        if(False):\n",
    "            print(\"Inference\")\n",
    "            print(\"Prediction: \")\n",
    "            print(\"Actual: \")\n",
    "            print(df_row[label])\n",
    "            print(labelProbabilities.idxmax())\n",
    "\n",
    "    return totalOutput, correctOutput"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59bd6f10-0bc9-4153-bf43-08c72bd7b8e8",
   "metadata": {},
   "source": [
    "## Printing\n",
    "This is just a helper method which outputs the results nicely"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "698abaf1-6d11-49e8-b0e9-96c77b681dd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Casey\n",
    "\n",
    "# Prints some useful information about the results\n",
    "\n",
    "def printResults(df_testSet, totalOutput, correctOutput, labelCol):\n",
    "    print(\"Distribution of TestSet Data:\")\n",
    "    print(df_testSet[labelCol].value_counts())\n",
    "    print()\n",
    "    print(\"Total Output Distribution: \")\n",
    "    print(totalOutput)\n",
    "    print()\n",
    "    print(\"CorrectOutput Distribution: \")\n",
    "    print(correctOutput)\n",
    "    print()\n",
    "    print(\"NumCorrect: \" + str(correctOutput.sum()))\n",
    "    print(\"Accuracy: \"  + str(correctOutput.sum() / len(df_testSet)))\n",
    "    # pres = numCorrect / len(df_testSet)\n",
    "    # print(\"Precision: \"  + str(pres)\n",
    "    # rec = \n",
    "    # print(\"Recall: \"  + str(rec)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e66b77c-4ce2-4220-834b-27105a986d71",
   "metadata": {},
   "source": [
    "## Putting it all together\n",
    "This method puts everything together and runs it to an output. It allows the user to input the bins and labels for the labelColumn and the percent of the train set to be used as a validation set (Manual validation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "c4e6233b-a3d6-4a45-852e-74f3c71675a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Casey\n",
    "\n",
    "# Uses all the previous functions to create a model from the county data, and evaluate the validation set (test_set)\n",
    "# Then outputs the results \"nicely\" formatted, and plots the actual vs expected prediction results\n",
    "\n",
    "def runNaiveBayes(labelCol, bins, labels, trainSetPerc):\n",
    "\n",
    "    og_df = pd.read_csv('archive/acs2017_county_data.csv')\n",
    "\n",
    "    df_binned = pruning_and_binning(og_df, bins, labels, labelCol)\n",
    "\n",
    "    df_train, df_test = get_trainingSet_and_testSet(df_binned, trainSetPerc)\n",
    "\n",
    "    label_marg, label_cond = build_naive_bayes_model(df_train, labelCol)\n",
    "\n",
    "    totalOutput, correctOutput = eval_with_bayes_model(df_test, label_marg, label_cond, labelCol, labels)\n",
    "\n",
    "    print(\"Results:\")\n",
    "    print(\"\\nLabel: \" + str(labelCol))\n",
    "    print(\"Bins: \" + str(bins))\n",
    "    print(\"Labels: \" + str(labels) + \"\\n\")\n",
    "    # print(\"\\nLabel distribution: \")\n",
    "    # print(label_marg)\n",
    "\n",
    "    printResults(df_test, totalOutput, correctOutput, labelCol)\n",
    "    \n",
    "    plotBayes(df_test, correctOutput, labelCol)\n",
    "    \n",
    "    print(label_cond)\n",
    "    \n",
    "    return df_test, totalOutput, correctOutput, labelCol"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "181e40de-b6c1-4216-b561-c94e2403eb9c",
   "metadata": {},
   "source": [
    "## Visually displaying it all\n",
    "This method was found either from stackOverflow or Kaggle, I can't remember which, and I adapted it to display the actual value for each label and the number the model correctly predicted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "496d51c7-1a01-4536-862f-fa73328a83e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Found from kaggle and adjusted to display my information\n",
    "def plotBayes(df_test, correctOutput, labelCol):\n",
    "    plt.rcParams[\"figure.figsize\"] = [7.00, 3.50]\n",
    "    plt.rcParams[\"figure.autolayout\"] = True\n",
    "\n",
    "    labels = df_test[labelCol].value_counts().sort_index().index\n",
    "    test_set_numbers = df_test[labelCol].value_counts()\n",
    "    test_set_numbers = test_set_numbers.sort_index()\n",
    "    output_numbers = correctOutput\n",
    "    output_number = output_numbers.sort_index()\n",
    "    # print(labels)\n",
    "    # print(test_set_numbers)\n",
    "    # print(output_numbers)\n",
    "\n",
    "    x = np.arange(len(labels))\n",
    "    width = 0.35\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    rects1 = ax.bar(x - width / 2, test_set_numbers, width, label='Actual')\n",
    "    rects2 = ax.bar(x + width / 2, output_numbers, width, label='Predicted')\n",
    "\n",
    "    ax.set_ylabel('Count')\n",
    "    ax.set_title('Number of ' + labelCol + ' bins correctly predicted')\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(labels)\n",
    "    ax.legend()\n",
    "\n",
    "    def autolabel(rects):\n",
    "       for rect in rects:\n",
    "          height = rect.get_height()\n",
    "          ax.annotate('{}'.format(height),\n",
    "             xy=(rect.get_x() + rect.get_width() / 2, height),\n",
    "             xytext=(0, 3), # 3 points vertical offset\n",
    "             textcoords=\"offset points\",\n",
    "             ha='center', va='bottom')\n",
    "\n",
    "    autolabel(rects1)\n",
    "    autolabel(rects2)\n",
    "\n",
    "    print(plt.show())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d45b3d8e-92c8-4a45-995d-f85590bbc5d3",
   "metadata": {},
   "source": [
    "## Ensemble???\n",
    "The next three boxes were thrown together when trying to add Bayes to the ensemble method but it really didn't work out. They didn't have the information my implementation needed. These are mainly fragments of previous code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c77b03fa-f005-4759-9480-ea50a5ae3256",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Casey\n",
    "\n",
    "# Generates a Naive Bayes model using test/train sets as input so that it could be used in an ensemble method\n",
    "\n",
    "def generateNaiveBayesModel(test_set, train_set, labels, bins, labelCol):\n",
    "    df_test = pruning_and_binning(test_set, bins, labels, labelCol)\n",
    "    df_train = pruning_and_binning(train_set, bins, labels, labelCol)\n",
    "\n",
    "    label_marg, label_cond = build_naive_bayes_model(df_train, labelCol)\n",
    "    return label_marg, label_cond, df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1d3f9056-997f-4e87-b978-72d9d801d074",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Casey\n",
    "\n",
    "# Returns the prediction for any given row, while taking as input \"the model\" or the marginal/conditional probability arrays, row and label\n",
    "# To be used with an ensemble method or the like\n",
    "\n",
    "def predictWithBayes(df_row, labelMarginals, labelConditionals, label):\n",
    "    labelProbabilities = labelMarginals.copy()\n",
    "\n",
    "    for i in range(len(labelProbabilities)):\n",
    "        for j in range(len(df_row)):\n",
    "            if(df_row.index[j] != label):\n",
    "                x = labelConditionals[i][j]\n",
    "                # print(x.loc[df_row[j]][0])\n",
    "                # print(df_row[j])\n",
    "                if df_row[j] in x:\n",
    "                    labelProbabilities[i] *= x.loc[df_row[j]][0]\n",
    "    return labelProbabilities.idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "32f69ce1-b903-4004-83f4-cdc50e52e15e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pruned & Binned\n",
      "\n",
      "Split & Shuffled\n",
      "\n",
      "Model Built\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Low'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Casey\n",
    "\n",
    "# Another approach so that it could be combined with another train/test set input\n",
    "# Mostly just experimenting/ bug fixing\n",
    "\n",
    "labelCol = 'IncomePerCap'\n",
    "bins = [0, 25000, math.inf]\n",
    "labels = ['Low', 'High']\n",
    "\n",
    "og_df = pd.read_csv('archive/acs2017_county_data.csv')\n",
    "\n",
    "df_binned = pruning_and_binning(og_df, bins, labels, labelCol)\n",
    "\n",
    "df_train, df_test = get_trainingSet_and_testSet(df_binned, 0.8)\n",
    "\n",
    "label_marg, label_cond = build_naive_bayes_model(df_train, labelCol)\n",
    "\n",
    "predictWithBayes(df_test.iloc[0], label_marg, label_cond, labelCol)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb2f8415-04ca-47c6-8947-f5555489f479",
   "metadata": {},
   "source": [
    "## Comparing years\n",
    "This code was meant to be used to train the model on the 2017 data and test on the 2015 but the datasets were too different to be easily compatible and I ran out of time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "653885cd-952f-4e3b-930b-4a2454f8ffa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Casey\n",
    "\n",
    "# Basically identical to the runNaiveBayes method except uses the 2017 data as the train set and the 2015 data as the test set\n",
    "# Doesn't work due to data set limitations :(\n",
    "\n",
    "def runNaiveBayesNewSet(labelCol, bins, labels):\n",
    "\n",
    "    og_df = pd.read_csv('archive/acs2017_county_data.csv')\n",
    "    og15_df = pd.read_csv('archive/acs2015_county_data.csv')\n",
    "\n",
    "    label_marg, label_cond, df_test = generateNaiveBayesModel(og15_df, og_df, labels, bins, labelCol)\n",
    "\n",
    "    totalOutput, correctOutput = eval_with_bayes_model(df_test, label_marg, label_cond, labelCol, labels)\n",
    "\n",
    "    print(\"Results:\")\n",
    "    print(\"\\nLabel: \" + str(labelCol))\n",
    "    print(\"Bins: \" + str(bins))\n",
    "    print(\"Labels: \" + str(labels) + \"\\n\")\n",
    "    # print(\"\\nLabel distribution: \")\n",
    "    # print(label_marg)\n",
    "\n",
    "    printResults(df_test, totalOutput, correctOutput, labelCol)\n",
    "    return df_test, totalOutput, correctOutput, labelCol"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e34ac303-6023-4dd4-a847-fc157638707b",
   "metadata": {},
   "source": [
    "## Finally running the things:\n",
    "This is where I actually ran everything. Most of it is commented it out to save runtime but I uncommented one of each of the lines at a time to see how each did. They're accuracy can be seen on the side. Most of the results are pretty evenly split otherwise. I didn't check recall & precision because when I looked at the results myself the misses were almost uniformly spread across the options so there wasn't one failing. And that's my data Inference exploration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "813019f1-a172-4d05-b79e-4d52e9dd9887",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pruned & Binned\n",
      "\n",
      "Split & Shuffled\n",
      "\n",
      "Model Built\n",
      "\n",
      "Running Inference Task\n",
      "\n",
      "Results:\n",
      "\n",
      "Label: IncomePerCap\n",
      "Bins: [0, 25000, inf]\n",
      "Labels: ['Low', 'High']\n",
      "\n",
      "Distribution of TestSet Data:\n",
      "High    339\n",
      "Low     305\n",
      "Name: IncomePerCap, dtype: int64\n",
      "\n",
      "Total Output Distribution: \n",
      "Low     305\n",
      "High    339\n",
      "dtype: int64\n",
      "\n",
      "CorrectOutput Distribution: \n",
      "Low     262\n",
      "High    296\n",
      "dtype: int64\n",
      "\n",
      "NumCorrect: 558\n",
      "Accuracy: 0.8664596273291926\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "[[State               \n",
      "Texas                   0.074615\n",
      "Iowa                    0.053077\n",
      "Minnesota               0.049231\n",
      "Virginia                0.048462\n",
      "Nebraska                0.047692\n",
      "Illinois                0.044615\n",
      "Kansas                  0.040000\n",
      "Wisconsin               0.036923\n",
      "North Dakota            0.033077\n",
      "Ohio                    0.033077\n",
      "New York                0.033077\n",
      "Indiana                 0.032308\n",
      "Pennsylvania            0.032308\n",
      "South Dakota            0.031538\n",
      "Colorado                0.026923\n",
      "Georgia                 0.024615\n",
      "Montana                 0.023846\n",
      "Florida                 0.023077\n",
      "California              0.022308\n",
      "Michigan                0.022308\n",
      "Kentucky                0.020000\n",
      "North Carolina          0.017692\n",
      "Washington              0.017692\n",
      "Missouri                0.016923\n",
      "Tennessee               0.016154\n",
      "Oklahoma                0.016154\n",
      "Alaska                  0.015385\n",
      "Oregon                  0.015385\n",
      "New Jersey              0.014615\n",
      "Maryland                0.014615\n",
      "Wyoming                 0.013846\n",
      "Louisiana               0.011538\n",
      "Massachusetts           0.010000\n",
      "Alabama                 0.009231\n",
      "Vermont                 0.009231\n",
      "Nevada                  0.007692\n",
      "Idaho                   0.007692\n",
      "South Carolina          0.007692\n",
      "Utah                    0.006923\n",
      "West Virginia           0.006923\n",
      "Arkansas                0.006923\n",
      "New Hampshire           0.006923\n",
      "Maine                   0.005385\n",
      "Connecticut             0.005385\n",
      "New Mexico              0.004615\n",
      "Hawaii                  0.004615\n",
      "Rhode Island            0.003846\n",
      "Mississippi             0.003846\n",
      "Arizona                 0.003846\n",
      "Delaware                0.003077\n",
      "District of Columbia    0.001538\n",
      "Puerto Rico             0.000769\n",
      "dtype: float64, TotalPop\n",
      "High        0.466154\n",
      "Low         0.297692\n",
      "Mid         0.237692\n",
      "dtype: float64, Hispanic\n",
      "Mid         0.380000\n",
      "High        0.339231\n",
      "Low         0.282308\n",
      "dtype: float64, White\n",
      "Mid      0.434615\n",
      "High     0.357692\n",
      "Low      0.209231\n",
      "dtype: float64, Black\n",
      "Low      0.373846\n",
      "Mid      0.354615\n",
      "High     0.273077\n",
      "dtype: float64, Native\n",
      "Low       0.494615\n",
      "High      0.306923\n",
      "Mid       0.200000\n",
      "dtype: float64, Asian\n",
      "High     0.460769\n",
      "Low      0.290000\n",
      "Mid      0.250769\n",
      "dtype: float64, VotingAgeCitizen\n",
      "High                0.470000\n",
      "Low                 0.298462\n",
      "Mid                 0.233077\n",
      "dtype: float64, IncomePerCap\n",
      "High            1.000000\n",
      "Low             0.000769\n",
      "dtype: float64, Poverty\n",
      "Low        0.593077\n",
      "Mid        0.350769\n",
      "High       0.057692\n",
      "dtype: float64, ChildPoverty\n",
      "Low             0.563846\n",
      "Mid             0.352308\n",
      "High            0.084615\n",
      "dtype: float64, Professional\n",
      "High            0.541538\n",
      "Mid             0.338462\n",
      "Low             0.121538\n",
      "dtype: float64, Service\n",
      "Low        0.429231\n",
      "Mid        0.345385\n",
      "High       0.226923\n",
      "dtype: float64, Office\n",
      "High      0.385385\n",
      "Mid       0.318462\n",
      "Low       0.297692\n",
      "dtype: float64, Construction\n",
      "Low             0.420000\n",
      "Mid             0.321538\n",
      "High            0.260000\n",
      "dtype: float64, Production\n",
      "Low           0.463077\n",
      "Mid           0.327692\n",
      "High          0.210769\n",
      "dtype: float64, Drive\n",
      "Low      0.373846\n",
      "Mid      0.350769\n",
      "High     0.276923\n",
      "dtype: float64, Carpool\n",
      "Low        0.411538\n",
      "Mid        0.380769\n",
      "High       0.209231\n",
      "dtype: float64, Transit\n",
      "High       0.391538\n",
      "Low        0.356923\n",
      "Mid        0.253077\n",
      "dtype: float64, Walk\n",
      "High    0.393846\n",
      "Mid     0.307692\n",
      "Low     0.300000\n",
      "dtype: float64, OtherTransp\n",
      "Low            0.356154\n",
      "Mid            0.328462\n",
      "High           0.316923\n",
      "dtype: float64, WorkAtHome\n",
      "High          0.450000\n",
      "Mid           0.362308\n",
      "Low           0.189231\n",
      "dtype: float64, MeanCommute\n",
      "Low            0.384615\n",
      "Mid            0.316923\n",
      "High           0.300000\n",
      "dtype: float64, Employed\n",
      "High        0.485385\n",
      "Low         0.270769\n",
      "Mid         0.245385\n",
      "dtype: float64, PrivateWork\n",
      "High           0.425385\n",
      "Mid            0.290769\n",
      "Low            0.285385\n",
      "dtype: float64, PublicWork\n",
      "Low           0.444615\n",
      "Mid           0.322308\n",
      "High          0.234615\n",
      "dtype: float64, SelfEmployed\n",
      "Low             0.365385\n",
      "High            0.354615\n",
      "Mid             0.281538\n",
      "dtype: float64, FamilyWork\n",
      "Low           0.436154\n",
      "Mid           0.360000\n",
      "High          0.205385\n",
      "dtype: float64, Unemployment\n",
      "Low             0.507692\n",
      "Mid             0.368462\n",
      "High            0.125385\n",
      "dtype: float64, MenPercent\n",
      "Low           0.466923\n",
      "Mid           0.311538\n",
      "High          0.223077\n",
      "dtype: float64], [State               \n",
      "Texas                   0.086072\n",
      "Georgia                 0.081377\n",
      "Kentucky                0.057903\n",
      "Missouri                0.051643\n",
      "Puerto Rico             0.046948\n",
      "Mississippi             0.046948\n",
      "North Carolina          0.046166\n",
      "Tennessee               0.043036\n",
      "Arkansas                0.040689\n",
      "Alabama                 0.035994\n",
      "Oklahoma                0.035994\n",
      "Virginia                0.032081\n",
      "Indiana                 0.031299\n",
      "Louisiana               0.028951\n",
      "West Virginia           0.028169\n",
      "Michigan                0.027387\n",
      "Ohio                    0.025822\n",
      "Florida                 0.022692\n",
      "Illinois                0.021909\n",
      "Kansas                  0.021909\n",
      "Idaho                   0.021127\n",
      "South Carolina          0.021127\n",
      "New Mexico              0.017997\n",
      "Colorado                0.016432\n",
      "Pennsylvania            0.014085\n",
      "California              0.012520\n",
      "Montana                 0.012520\n",
      "Utah                    0.011737\n",
      "South Dakota            0.010172\n",
      "Oregon                  0.010172\n",
      "Washington              0.009390\n",
      "Nebraska                0.008607\n",
      "Arizona                 0.007825\n",
      "New York                0.007042\n",
      "Wisconsin               0.007042\n",
      "Minnesota               0.005477\n",
      "Maine                   0.005477\n",
      "Alaska                  0.004695\n",
      "Iowa                    0.003912\n",
      "North Dakota            0.003130\n",
      "Nevada                  0.003130\n",
      "Maryland                0.002347\n",
      "Wyoming                 0.002347\n",
      "New Jersey              0.001565\n",
      "Vermont                 0.001565\n",
      "Massachusetts           0.000782\n",
      "New Hampshire           0.000782\n",
      "Connecticut             0.000782\n",
      "Hawaii                  0.000782\n",
      "Rhode Island            0.000782\n",
      "Delaware                0.000782\n",
      "District of Columbia    0.000782\n",
      "dtype: float64, TotalPop\n",
      "Mid         0.426448\n",
      "Low         0.366980\n",
      "High        0.208138\n",
      "dtype: float64, Hispanic\n",
      "Low         0.383412\n",
      "High        0.323161\n",
      "Mid         0.294992\n",
      "dtype: float64, White\n",
      "Low      0.461659\n",
      "High     0.290297\n",
      "Mid      0.249609\n",
      "dtype: float64, Black\n",
      "High     0.399844\n",
      "Low      0.306729\n",
      "Mid      0.294992\n",
      "dtype: float64, Native\n",
      "Low       0.479656\n",
      "High      0.338811\n",
      "Mid       0.183099\n",
      "dtype: float64, Asian\n",
      "Low      0.532864\n",
      "Mid      0.283255\n",
      "High     0.185446\n",
      "dtype: float64, VotingAgeCitizen\n",
      "Mid                 0.432707\n",
      "Low                 0.364632\n",
      "High                0.204225\n",
      "dtype: float64, IncomePerCap\n",
      "Low             1.000000\n",
      "High            0.000782\n",
      "dtype: float64, Poverty\n",
      "High       0.607199\n",
      "Mid        0.338028\n",
      "Low        0.056338\n",
      "dtype: float64, ChildPoverty\n",
      "High            0.588419\n",
      "Mid             0.334898\n",
      "Low             0.078247\n",
      "dtype: float64, Professional\n",
      "Low             0.564945\n",
      "Mid             0.336463\n",
      "High            0.100156\n",
      "dtype: float64, Service\n",
      "High       0.446792\n",
      "Mid        0.306729\n",
      "Low        0.248044\n",
      "dtype: float64, Office\n",
      "Low       0.378717\n",
      "Mid       0.329421\n",
      "High      0.293427\n",
      "dtype: float64, Construction\n",
      "High            0.413928\n",
      "Mid             0.338028\n",
      "Low             0.249609\n",
      "dtype: float64, Production\n",
      "High          0.458529\n",
      "Mid           0.330986\n",
      "Low           0.212050\n",
      "dtype: float64, Drive\n",
      "High     0.392019\n",
      "Mid      0.324726\n",
      "Low      0.284820\n",
      "dtype: float64, Carpool\n",
      "High       0.434272\n",
      "Mid        0.294992\n",
      "Low        0.272300\n",
      "dtype: float64, Transit\n",
      "Low        0.500782\n",
      "Mid        0.305164\n",
      "High       0.195618\n",
      "dtype: float64, Walk\n",
      "Low     0.428795\n",
      "Mid     0.312989\n",
      "High    0.259781\n",
      "dtype: float64, OtherTransp\n",
      "Low            0.384977\n",
      "High           0.337246\n",
      "Mid            0.279343\n",
      "dtype: float64, WorkAtHome\n",
      "Low           0.517997\n",
      "Mid           0.287167\n",
      "High          0.196401\n",
      "dtype: float64, MeanCommute\n",
      "High           0.359937\n",
      "Mid            0.349765\n",
      "Low            0.291862\n",
      "dtype: float64, Employed\n",
      "Mid         0.422535\n",
      "Low         0.389671\n",
      "High        0.189358\n",
      "dtype: float64, PrivateWork\n",
      "Low            0.387324\n",
      "Mid            0.361502\n",
      "High           0.252739\n",
      "dtype: float64, PublicWork\n",
      "High          0.436620\n",
      "Mid           0.339593\n",
      "Low           0.225352\n",
      "dtype: float64, SelfEmployed\n",
      "Mid             0.389671\n",
      "Low             0.338028\n",
      "High            0.273865\n",
      "dtype: float64, FamilyWork\n",
      "Low           0.446009\n",
      "Mid           0.322379\n",
      "High          0.233177\n",
      "dtype: float64, Unemployment\n",
      "High            0.525822\n",
      "Mid             0.305947\n",
      "Low             0.169797\n",
      "dtype: float64, MenPercent\n",
      "Low           0.485915\n",
      "High          0.273865\n",
      "Mid           0.241784\n",
      "dtype: float64]]\n"
     ]
    }
   ],
   "source": [
    "# Casey\n",
    "\n",
    "# Where I actually used the methods/ got actual results\n",
    "\n",
    "# Experimenting with number of bins for the labels & Valication Set\n",
    "\n",
    "# Trying different size bins, divides lables evenly by frequency across the bins\n",
    "labelCol = 'IncomePerCap'\n",
    "# labels = ['Low', 'Low-Med', 'Med', 'High-Med', 'High'] #~52% Accuracy\n",
    "# labels = ['Low', 'Low-Med','High-Med', 'High'] #~63% Accuracy\n",
    "# labels = ['Low', 'Med', 'High'] #~74% Accuracy\n",
    "# labels = ['Low', 'High'] #~87% Accuracy\n",
    "# df_discard, cut_bin = pd.qcut(x = og_df[labelCol], q = len(labels), labels = labels, retbins = True)\n",
    "# runNaiveBayes(labelCol, cut_bin, labels, 0.8) \n",
    "\n",
    "\n",
    "labels = ['Low', 'High']\n",
    "# Experimenting with the exact breakpoint to best divide the set\n",
    "runNaiveBayes(labelCol, [0, 25000, math.inf], labels, 0.8) #87%  ****Seems the best set up****\n",
    "# runNaiveBayes(labelCol, [0, 30000, math.inf], labels, 0.8) #85%\n",
    "# runNaiveBayes(labelCol, [0, 20000, math.inf], labels, 0.8) #87%\n",
    "\n",
    "# Experiments with the best size for the validation set\n",
    "# runNaiveBayes(labelCol, [0, 25000, math.inf], labels, 0.8) #87%\n",
    "# runNaiveBayes(labelCol, [0, 25000, math.inf], labels, 0.95) #87%\n",
    "# runNaiveBayes(labelCol, [0, 25000, math.inf], labels, 0.65) #85%\n",
    "\n",
    "# I tried to train Naive Bayes on the 2017 set and test on the 2015 set\n",
    "# This didn't work because the entries in some columns were formatted differently and also somce column names were different (that could be fixed though)\n",
    "# df_test, totalOutput, correctOutput, labelCol = runNaiveBayesNewSet(labelCol, [0, 25000, math.inf], labels) #N/A"
   ]
  }
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