PartI_featureengineering, EDA.ipynb

{
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   "id": "82f2c5d9-4c84-4913-ac1e-dfe3a92afdaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Import packages\n",
    "import numpy as np \n",
    "\n",
    "# data processing\n",
    "import pandas as pd \n",
    "\n",
    "# data visualization\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "### feature engineering\n",
    "import country_converter as coco\n",
    "import scipy.stats as ss\n",
    "from scipy.stats import chi2_contingency\n",
    "import missingno as msno\n",
    "from itertools import product\n",
    "from sklearn.preprocessing import OrdinalEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7097c19e-0d0b-4056-b8a6-cf7025463bc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20036 entries, 0 to 20035\n",
      "Columns: 355 entries, Time from Start to Finish (seconds) to Q35_B_OTHER\n",
      "dtypes: int64(1), object(354)\n",
      "memory usage: 54.3+ MB\n"
     ]
    }
   ],
   "source": [
    "kaggle=pd.read_csv(\"kaggle_survey_2020_responses.csv\")\n",
    "kaggle.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05491d3e-a0a7-4df3-8843-28576c2a0751",
   "metadata": {},
   "outputs": [],
   "source": [
    "### DATA WRANGLING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1ff09d9a-a7e3-4aa3-a36d-371835c7d92d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5670, 355)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### First step: I am only interested in four job titles for this research. So, here is the subset of the originial data\n",
    "k_int=kaggle[(kaggle['Q5'] == 'Data Analyst') | (kaggle['Q5'] == 'Data Scientist') | (kaggle['Q5'] == 'Data Engineer') | (kaggle['Q5'] == 'Machine Learning Engineer')]\n",
    "k_int.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7ca361ac-c9cf-4390-8029-4c7a8a1b22bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Some questions in the survey are irrelevant for this project. For instance, certain questions are asked only for BI analysts. \n",
    "## The main focus here is to predict who is a data scientist or a data engineers based on the features. I want to depict the differences betweeen data scientists\n",
    "### and data engineers based on various factors.\n",
    "k_int= k_int.iloc[: , 1:]\n",
    "k_int=k_int.drop(k_int.loc[:, 'Q8':'Q14_OTHER'].columns, axis = 1)\n",
    "k_int=k_int.drop(k_int.loc[:, 'Q18_Part_1':'Q18_OTHER'].columns, axis = 1)\n",
    "k_int=k_int.drop(k_int.loc[:, 'Q26_A_Part_1':'Q27_A_OTHER'].columns, axis = 1)\n",
    "k_int=k_int.drop(k_int.loc[:, 'Q29_A_Part_1':'Q30'].columns, axis = 1)\n",
    "k_int=k_int.drop(k_int.loc[:, 'Q31_A_Part_1':'Q32'].columns, axis = 1)\n",
    "### Removing supplementary questions: NaN is btw 85% -95%\n",
    "k_int=k_int.drop(k_int.loc[:, 'Q26_B_Part_1':'Q35_B_OTHER'].columns, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "103fc347-9068-4c75-bef5-fbcff5a7fed9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q4</th>\n",
       "      <th>Q5</th>\n",
       "      <th>Q6</th>\n",
       "      <th>Q7_Part_1</th>\n",
       "      <th>Q7_Part_2</th>\n",
       "      <th>Q7_Part_3</th>\n",
       "      <th>Q7_Part_4</th>\n",
       "      <th>...</th>\n",
       "      <th>Q39_Part_3</th>\n",
       "      <th>Q39_Part_4</th>\n",
       "      <th>Q39_Part_5</th>\n",
       "      <th>Q39_Part_6</th>\n",
       "      <th>Q39_Part_7</th>\n",
       "      <th>Q39_Part_8</th>\n",
       "      <th>Q39_Part_9</th>\n",
       "      <th>Q39_Part_10</th>\n",
       "      <th>Q39_Part_11</th>\n",
       "      <th>Q39_OTHER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12440</th>\n",
       "      <td>25-29</td>\n",
       "      <td>Man</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>Bachelor’s degree</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>5-10 years</td>\n",
       "      <td>Python</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SQL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>YouTube (Kaggle YouTube, Cloud AI Adventures, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Blogs (Towards Data Science, Analytics Vidhya,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 144 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Q1   Q2         Q3                 Q4            Q5          Q6  \\\n",
       "12440  25-29  Man  Indonesia  Bachelor’s degree  Data Analyst  5-10 years   \n",
       "\n",
       "      Q7_Part_1 Q7_Part_2 Q7_Part_3 Q7_Part_4  ... Q39_Part_3 Q39_Part_4  \\\n",
       "12440    Python       NaN       SQL       NaN  ...        NaN        NaN   \n",
       "\n",
       "      Q39_Part_5                                         Q39_Part_6  \\\n",
       "12440        NaN  YouTube (Kaggle YouTube, Cloud AI Adventures, ...   \n",
       "\n",
       "      Q39_Part_7                                         Q39_Part_8  \\\n",
       "12440        NaN  Blogs (Towards Data Science, Analytics Vidhya,...   \n",
       "\n",
       "      Q39_Part_9 Q39_Part_10 Q39_Part_11 Q39_OTHER  \n",
       "12440        NaN         NaN         NaN       NaN  \n",
       "\n",
       "[1 rows x 144 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_int.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0b99d983-ee6d-4179-b338-e2b463379ae4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Q1             object\n",
       "Q2             object\n",
       "Q3             object\n",
       "Q4             object\n",
       "Q5             object\n",
       "                ...  \n",
       "Q39_Part_8     object\n",
       "Q39_Part_9     object\n",
       "Q39_Part_10    object\n",
       "Q39_Part_11    object\n",
       "Q39_OTHER      object\n",
       "Length: 144, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_int.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f410ccf6-e5be-41dc-aa4a-1ca663f65255",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### MISSING VALUES - just checking it overall and make a general change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "97b25abd-8270-4ea8-84a8-22ee3187a7c3",
   "metadata": {},
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    {
     "name": "stdout",
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     "text": [
      "Q1              True\n",
      "Q2              True\n",
      "Q3              True\n",
      "Q4              True\n",
      "Q5              True\n",
      "               ...  \n",
      "Q39_Part_8     False\n",
      "Q39_Part_9     False\n",
      "Q39_Part_10    False\n",
      "Q39_Part_11    False\n",
      "Q39_OTHER      False\n",
      "Length: 144, dtype: bool\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5670 entries, 1 to 20035\n",
      "Data columns (total 14 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   Q1         5670 non-null   object\n",
      " 1   Q2         5670 non-null   object\n",
      " 2   Q3         5670 non-null   object\n",
      " 3   Q4         5670 non-null   object\n",
      " 4   Q5         5670 non-null   object\n",
      " 5   Q6         5618 non-null   object\n",
      " 6   Q7_Part_1  4964 non-null   object\n",
      " 7   Q15        5167 non-null   object\n",
      " 8   Q20        5226 non-null   object\n",
      " 9   Q21        5177 non-null   object\n",
      " 10  Q22        5108 non-null   object\n",
      " 11  Q24        4945 non-null   object\n",
      " 12  Q25        4876 non-null   object\n",
      " 13  Q38        4443 non-null   object\n",
      "dtypes: object(14)\n",
      "memory usage: 664.5+ KB\n"
     ]
    }
   ],
   "source": [
    "## Second step: deleting the features with more than 70% missing values, naming the columns, and dealing with the missing values in the remaining features\n",
    "### Higher than 50% missing values = True value - applying a missing value threshold\n",
    "mask=k_int.isna().sum()/len(k_int) < 0.3\n",
    "print(mask)\n",
    "reduced_kint=k_int.loc[:, mask]\n",
    "reduced_kint.shape\n",
    "reduced_kint.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3ad1c0fd-cf39-420f-8249-1ba885bd0126",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\berna\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:5039: 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",
      "  return super().rename(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['age', 'gender', 'country', 'educ', 'title', 'experience', 'Python_or',\n",
       "       'ML_exp', 'comp_size', 'team', 'ML_buss', 'salary', 'ML_spent',\n",
       "       'data_tool'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reduced_kint.rename(columns={'Q1':'age','Q2':'gender','Q3':'country','Q4':'educ','Q5':'title','Q6':'experience','Q7_Part_1':'Python_or','Q15':'ML_exp', 'Q20':'comp_size','Q21':'team', 'Q22':'ML_buss','Q24':'salary','Q25':'ML_spent','Q38':'data_tool'}, inplace=True)\n",
    "reduced_kint.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d2441151-21bf-4e5d-8d0d-4e2f1b4d2569",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age           0.000000\n",
       "gender        0.000000\n",
       "country       0.000000\n",
       "educ          0.000000\n",
       "title         0.000000\n",
       "experience    0.009171\n",
       "Python_or     0.124515\n",
       "ML_exp        0.088713\n",
       "comp_size     0.078307\n",
       "team          0.086949\n",
       "ML_buss       0.099118\n",
       "salary        0.127866\n",
       "ML_spent      0.140035\n",
       "data_tool     0.216402\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reduced_kint.isna().sum()/len(reduced_kint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcd0daa7-9a6e-4844-a622-286a9aec087b",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Rearrange the features: Python_or, ML_tool, and ML_algorithm, because NaN's means 0(no), the strings is equivalent to 1(yes).\n",
    "reduced_kint['Python_or'] = reduced_kint['Python_or'].replace(np.nan,0)\n",
    "reduced_kint['Python_or'] = reduced_kint['Python_or'].replace(['Python'],1)\n",
    "reduced_kint['Python_or'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c903b8d-3c42-4929-b6ab-8404edb8b5ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = reduced_kint.copy()\n",
    "df.isna().sum()/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe259696-27e7-4913-8f28-e90b2ce0d667",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9970b395-2141-4557-91da-cd993e0cc9b1",
   "metadata": {},
   "source": [
    "### FEATURE ENGINEERING"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de750490-c7fa-4d95-8501-547f3b1eba32",
   "metadata": {},
   "source": [
    "### All features are categorical in the dataset: This step includes transforming the features by creating new features, recategorizing the existing categories, and get the dummies for the analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad0224d3-98f0-4ff9-8aa7-7a7a54a49068",
   "metadata": {},
   "outputs": [],
   "source": [
    "### FEATURE ENGINEERING: COUNTRY\n",
    "### SURVEY QUESTION: \"In which country do you currently reside?\"\n",
    "\n",
    "# The feature 'Country' contains too many categories. The better approach is to convert countries to continents by creating a new feature.\n",
    "df.country.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9ce03eb-7e86-444d-a337-32d459defe65",
   "metadata": {},
   "outputs": [],
   "source": [
    "converter = coco.CountryConverter()\n",
    "df['region']=converter.convert(names = df['country'], to = 'continent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d2d3514-ef12-4627-8b3a-c28169b5a11d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['region'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ab9618e-874e-413b-baeb-82c7a30f7f92",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Due to the inbalanced groups in this feature, I regroup the small groups and merge them under the sub-category 'Other'.\n",
    "df['region'] = df['region'].replace(['Oceania'],'not found')\n",
    "df['region'] = df['region'].replace(['Africa'],'not found')\n",
    "df['region'] = df['region'].replace(['not found'],'Other')\n",
    "df= df.drop('country', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cad47186-6458-48c3-a4b0-6010287a6aa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['region'] = df['region'].replace(['America'],'1')\n",
    "df['region'] = df['region'].replace(['Europe'],'2')\n",
    "df['region'] = df['region'].replace(['Asia'],'3')\n",
    "df['region'] = df['region'].replace(['Other'],'0')\n",
    "df['region'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87547915-e2ab-4733-bd8d-65affb93145d",
   "metadata": {},
   "outputs": [],
   "source": [
    "### At this point, before moving on with feature engineering, let's check the relationship among categories with Chi Square.\n",
    "#### CHI SQUARE - CORRELATIONS\n",
    "cat=('age','gender','educ','title','experience','ML_exp','comp_size','team','ML_buss','salary','ML_spent','data_tool','region')\n",
    "## Let us jump to Chi-Square test\n",
    "## Creating all possible combinations between the above two variables list\n",
    "cat_var_prod = list(product(cat,cat, repeat = 1))\n",
    "print(cat_var_prod)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab1d88ce-d916-45a9-bcbc-67186df4f173",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = []\n",
    "for i in cat_var_prod:\n",
    "    if i[0] != i[1]:\n",
    "        result.append((i[0],i[1],list(ss.chi2_contingency(pd.crosstab(\n",
    "                            df[i[0]], df[i[1]])))[1]))\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b14b44f-d765-4225-9cf1-5a7d657cd59e",
   "metadata": {},
   "outputs": [],
   "source": [
    "chi_test_output = pd.DataFrame(result, columns = ['var1', 'var2', 'coeff'])\n",
    "# Using pivot function to convert the above DataFrame into a crosstab\n",
    "x= chi_test_output.pivot(index='var1',columns='var2', values='coeff')\n",
    "x.round(decimals=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb31fa80-406f-430e-9cb5-f7bfdfb70952",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Almost all features are correlated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a4e4e93-953c-47fa-9e88-835a7cf1371a",
   "metadata": {},
   "outputs": [],
   "source": [
    "### FEATURE ENGINEERING_ TARGET FEATURE: SALARY\n",
    "### SURVEY QUESTION: \"What is your current yearly compensation (approximate $USD)?\"\n",
    "df['salary'].value_counts(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efe44265-4f66-4940-b4e6-2a267dd745e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Regrouping salary based on the number of individuals in each group (group size is considered)\n",
    "### salary group 1: 0-999\n",
    "### salary group 2: 1,000-9,999\n",
    "### salary group 3: 10,000-39,999\n",
    "### salary group 4: 40,000-99,999\n",
    "### salary group 5: above 100,000\n",
    "\n",
    "df[\"salary\"] = df[\"salary\"].map({\"$0-999\": \"1\", \"1,000-1,999\": \"2\", \"2,000-2,999\":\"2\", \"3,000-3,999\":\"2\",\"4,000-4,999\":\"2\", \"5,000-7,499\":\"2\",\"7,500-9,999\":\"2\",\\\n",
    "                                \"10,000-14,999\":\"3\",\"15,000-19,999\":\"3\",\"20,000-24,999\":\"3\",\"25,000-29,999\":\"3\",\"30,000-39,999\":\"3\",\"40,000-49,999\":\"4\",\"50,000-59,999\":\"4\",\\\n",
    "                                \"60,000-69,999\":\"4\",\"70,000-79,999\":\"4\",\"80,000-89,999\":\"4\",\"90,000-99,999\":\"4\",\"100,000-124,999\":\"5\",\"125,000-149,999\":\"5\",\"150,000-199,999\":\"5\",\\\n",
    "                                \"200,000-249,999\":\"5\", \"250,000-299,999\":\"5\", \"300,000-500,000\":\"5\",\"> $500,000\":\"5\"})\n",
    "df['salary'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d96349c-a4f1-41f7-8905-d8c3ea0a36ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "### FEATURE ENGINEERING: AGE\n",
    "### SURVEY QUESTION: \"What is your age (# years)?\"\n",
    "### recoded as 20s=1, 30s=2, 40s=3, 50s=4, 60s&above=5\n",
    "df['age'].unique()\n",
    "###rename categories in age\n",
    "df[\"age\"] = df[\"age\"].map({\"18-21\": \"1\", \"22-24\": \"1\", '25-29':\"1\", \"30-34\":\"2\",\"35-39\":\"2\", \"40-44\":\"3\",\"45-49\":\"3\",\"50-54\":\"4\",\"55-59\":\"4\",\"60-69\":\"5\",\"70\":\"5\"})\n",
    "df['age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66214821-96b9-4fa1-b784-3f574dc2a805",
   "metadata": {},
   "outputs": [],
   "source": [
    "### FEATURE ENGINEERING: GENDER\n",
    "### SURVEY QUESTION: \"What is your gender?\"\n",
    "df.gender.value_counts()\n",
    "df['gender'] = df['gender'].replace(['Prefer not to say'],'Other')\n",
    "df['gender'] = df['gender'].replace(['Prefer to self-describe'],'Other')\n",
    "df['gender'] = df['gender'].replace(['Nonbinary'],'Other')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78889bbd-72d6-4617-ace7-5aea1649e92e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['gender'] = df['gender'].replace(['Other'],'0')\n",
    "df['gender'] = df['gender'].replace(['Man'],'1')\n",
    "df['gender'] = df['gender'].replace(['Woman'],'2')\n",
    "df.gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "363b9274-e998-4d38-9407-183de31d725e",
   "metadata": {},
   "outputs": [],
   "source": [
    "### FEATURE ENGINEERING: EDUCATION\n",
    "### SURVEY QUESTION: \"What is the highest level of formal education that you have attained or plan to attain within the next 2 years?\"\n",
    "df['educ'].value_counts()\n",
    "df[\"educ\"] = df[\"educ\"].map({\"Master’s degree\": \"MA\", \"Bachelor’s degree\": \"BA\", 'Doctoral degree':\"PhD\", \"Some college/university study without earning a bachelor’s degree\":\"somedegree\",\"Professional degree\":\"prof\", \"I prefer not to answer\":\"noansw\",\"No formal education past high school\":\"high\"})\n",
    "df['educ'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d22eac96-b898-47ab-804d-8d73c9983f26",
   "metadata": {},
   "outputs": [],
   "source": [
    "### There are still too many sub-categories for education. When I get the dummies for this feature, it will be uncontrollable.\n",
    "### Also, another problem of too many categories is related to the inbalanced sub-category sizes. \n",
    "### However, before make a move, I need to understand its relation to the target feature.\n",
    "sns.histplot(binwidth=1, x=\"educ\", hue=\"salary\", data=df, stat=\"count\", multiple=\"stack\")\n",
    "plt.xticks(rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8492c70a-a695-40a6-bd22-9db98f057e83",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Based on the visualization, it is clear that those who do not prefer to answer the question are in salary group 1. So, we can merge two groups!\n",
    "### Also, due to the inbalanced category sizes, I decided to collapse all four groups into one: OTHER (educ_somedegree+educ_prof+educ_high+educ_noanswer)\n",
    "df['educ'] = df['educ'].replace(['somedegree'],'Other')\n",
    "df['educ'] = df['educ'].replace(['prof'],'Other')\n",
    "df['educ'] = df['educ'].replace(['noansw'],'Other')\n",
    "df['educ'] = df['educ'].replace(['high'],'Other')\n",
    "df.educ.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbea4df4-28c9-49ff-a370-6c1fb95ea51e",
   "metadata": {},
   "outputs": [],
   "source": [
    "### recoding Other=0, BA=1, MA=2, PhD=3\n",
    "df['educ'] = df['educ'].replace(['Other'],'0')\n",
    "df['educ'] = df['educ'].replace(['BA'],'1')\n",
    "df['educ'] = df['educ'].replace(['MA'],'2')\n",
    "df['educ'] = df['educ'].replace(['PhD'],'3')\n",
    "df.educ.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6d98730-35c5-4ebe-ba1a-9560ff758f25",
   "metadata": {},
   "outputs": [],
   "source": [
    "### FEATURE ENGINEERING: MACHINE LEARNING EXPERIENCE\n",
    "### SURVEY QUESTION: \"For how many years have you used machine learning methods?\"\n",
    "df.ML_exp.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a68cd42e-4039-4abb-863e-30a42eaafc84",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ML_exp'] = df['ML_exp'].replace(['Under 1 year'],'newbie')\n",
    "df['ML_exp'] = df['ML_exp'].replace(['1-2 years'],'junior')\n",
    "df['ML_exp'] = df['ML_exp'].replace(['2-3 years'],'mid')\n",
    "df['ML_exp'] = df['ML_exp'].replace(['3-4 years'],'mid')\n",
    "df['ML_exp'] = df['ML_exp'].replace(['4-5 years'],'senior')\n",
    "df['ML_exp'] = df['ML_exp'].replace(['5-10 years'],'senior')\n",
    "df['ML_exp'] = df['ML_exp'].replace(['10-20 years'],'lead')\n",
    "df['ML_exp'] = df['ML_exp'].replace(['20 or more years'],'lead')\n",
    "df.ML_exp.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed717e7f-44f9-44b5-91a2-9b3dae20766d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.histplot(binwidth=1, x=\"ML_exp\", hue=\"experience\", data=df, stat=\"count\", multiple=\"stack\")\n",
    "plt.xticks(rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d8bbbec",
   "metadata": {},
   "outputs": [],
   "source": [
    "#After checking the experience in coding/programming, I will decide what to do with the group of 'I do not use machine learning methods' under the ML_experience feature\n",
    "df['ML_exp'] = df['ML_exp'].replace(['I do not use machine learning methods'],'no_MLexp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ceefec5b-1438-4399-8d88-d9006865eac7",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Recoding\n",
    "df['ML_exp'] = df['ML_exp'].replace(['no_MLexp'],'0')\n",
    "df['ML_exp'] = df['ML_exp'].replace(['newbie'],'1')\n",
    "df['ML_exp'] = df['ML_exp'].replace(['junior'],'2')\n",
    "df['ML_exp'] = df['ML_exp'].replace(['mid'],'3')\n",
    "df['ML_exp'] = df['ML_exp'].replace(['senior'],'4')\n",
    "df['ML_exp'] = df['ML_exp'].replace(['lead'],'5')\n",
    "df.ML_exp.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "242c03bd-6dd2-4d61-ac0e-8c9075adce17",
   "metadata": {},
   "outputs": [],
   "source": [
    "## FEATURE ENGINEERING: EXPERIENCE\n",
    "### SURVEY QUESTION: \"For how many years have you been writing code and/or programming?\"\n",
    "df.experience.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a6e3f8c-5554-4a15-87db-4510026c3f24",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['experience'] = df['experience'].replace(['< 1 years'],'junior_coder')\n",
    "df['experience'] = df['experience'].replace(['1-2 years'],'junior_coder')\n",
    "df['experience'] = df['experience'].replace(['3-5 years'],'midlev_coder')\n",
    "df['experience'] = df['experience'].replace(['5-10 years'],'midlev_coder')\n",
    "df['experience'] = df['experience'].replace(['10-20 years'],'senior_coder')\n",
    "df['experience'] = df['experience'].replace(['20+ years'],'senior_coder')\n",
    "df.experience.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63059c23-2056-4a29-a4f1-4c0b5f48ec6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.histplot(binwidth=1, x=\"ML_exp\", hue=\"experience\", data=df, stat=\"count\", multiple=\"stack\")\n",
    "plt.xticks(rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0d026e4-e6b9-4219-a570-b2d11553cbae",
   "metadata": {},
   "outputs": [],
   "source": [
    "### It seems like only those who are newbies in ML_experience have no experience in writing codes.\n",
    "df['experience'] = df['experience'].replace(['I have never written code'],'no_coder')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b88d82b3-67bb-4b83-8f36-c2793381aa1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Recode: Ordinal\n",
    "df['experience'] = df['experience'].replace(['no_coder'],'0')\n",
    "df['experience'] = df['experience'].replace(['junior_coder'],'1')\n",
    "df['experience'] = df['experience'].replace(['midlev_coder'],'2')\n",
    "df['experience'] = df['experience'].replace(['senior_coder'],'3')\n",
    "df.experience.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa012597-8dac-4d3a-a60b-80d9b4679181",
   "metadata": {},
   "outputs": [],
   "source": [
    "### FEATURE ENGINEERING: TITLE\n",
    "### SURVEY QUESTION: \"Select the title most similar to your current role (or most recent title if retired):\"\n",
    "df.title.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa929753-624c-4c6f-8a74-f2fe919c46ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Let's get rid of the white space in between the letters\n",
    "df.title = df.title.str.replace(\" \", \"\", regex=True)\n",
    "df.title.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83200d72-8992-4f34-ac8e-36311bfe3aac",
   "metadata": {},
   "outputs": [],
   "source": [
    "### TITLE is the TARGET FEATURE.\n",
    "df['title'] = df['title'].replace(['DataEngineer'],'0')\n",
    "df['title'] = df['title'].replace(['DataAnalyst'],'0')\n",
    "df['title'] = df['title'].replace(['MachineLearningEngineer'],'0')\n",
    "df['title'] = df['title'].replace(['DataScientist'],'1')\n",
    "df.title.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ba30b71-8343-486f-aee0-ba1ee301aca7",
   "metadata": {},
   "outputs": [],
   "source": [
    "### FEATURE ENGINEERING: COMPANY SIZE\n",
    "### SURVEY QUESTION: \"What is the size of the company where you are employed?\"\n",
    "df.comp_size.value_counts()\n",
    "df[\"comp_size\"] = df[\"comp_size\"].map({\"0-49 employees\": \"small_comp\", \"50-249 employees\": \"small_comp\", \"250-999 employees\":\"midsize_comp\", \"1000-9,999 employees\":\"midsize_comp\",\"10,000 or more employees\":\"big_comp\"})\n",
    "df['comp_size'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbccd63f-1b80-4597-9261-7ab5350cee02",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Recode Ordinal\n",
    "df['comp_size'] = df['comp_size'].replace(['small_comp'],'1')\n",
    "df['comp_size'] = df['comp_size'].replace(['midsize_comp'],'2')\n",
    "df['comp_size'] = df['comp_size'].replace(['big_comp'],'3')\n",
    "df.comp_size.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d44fc10-82d7-4673-a99f-d65a51de068e",
   "metadata": {},
   "outputs": [],
   "source": [
    "### FEATURE ENGINEERING: TEAM SIZE\n",
    "### SURVEY QUESTION: \"Approximately how many individuals are responsible for data science workloads at your place of business?\"\n",
    "### Recode solo_or_small(0 and 1-2)=1, small_mid(3-4 and 5-9)=2, mid_big(10-14, 15-19, and 20)=3\n",
    "df.team.value_counts()\n",
    "df['team'] = df['team'].replace(['0'],'1')\n",
    "df['team'] = df['team'].replace(['1-2'],'1')\n",
    "df['team'] = df['team'].replace(['3-4'],'2')\n",
    "df['team'] = df['team'].replace(['5-9'],'2')\n",
    "df['team'] = df['team'].replace(['10-14'],'3')\n",
    "df['team'] = df['team'].replace(['15-19'],'3')\n",
    "df['team'] = df['team'].replace(['20'],'3')\n",
    "df.team.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c71ae01-8001-4e0a-aaba-d6328900e0e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "### FEATURE ENGINEERING: ML's INCORPORATION INTO BUSINESS\n",
    "### SURVEY QUESTION: \"Does your current employer incorporate machine learning methods into their business?\"\n",
    "\n",
    "df.ML_buss.value_counts()\n",
    "\n",
    "### Just checking the relationship between individuals' ML experience and companies' structure\n",
    "sns.histplot(binwidth=1, x=\"ML_exp\", hue=\"ML_buss\", data=df, stat=\"count\", multiple=\"stack\")\n",
    "plt.xticks(rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f9affb4-eb59-4f16-8fbc-21d9e3758a7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "###It seems like the higher the individuals' experience in ML, the better they are informed about the company's structure wrt ML.\n",
    "### Recoding: no use & I do not know=0, exploring=1, insights=2, recently_started=3, established = 4\n",
    "df['ML_buss'] = df['ML_buss'].replace(['We have well established ML methods (i.e., models in production for more than 2 years)'],'4')\n",
    "df['ML_buss'] = df['ML_buss'].replace(['We recently started using ML methods (i.e., models in production for less than 2 years)'],'3')\n",
    "df['ML_buss'] = df['ML_buss'].replace(['We are exploring ML methods (and may one day put a model into production)'],'1')\n",
    "df['ML_buss'] = df['ML_buss'].replace(['We use ML methods for generating insights (but do not put working models into production)'],'2')\n",
    "df['ML_buss'] = df['ML_buss'].replace(['No (we do not use ML methods)'],'0')\n",
    "df['ML_buss'] = df['ML_buss'].replace(['I do not know'],'0')\n",
    "df.ML_buss.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a2957e0-e927-4203-92d0-7e00eeaf2622",
   "metadata": {},
   "outputs": [],
   "source": [
    "### FEATURE ENGINEERING: ML SPENDING\n",
    "### SURVEY QUESTION: \"Approximately how much money have you (or your team) spent on machine learning and/or cloud computing services at home (or at work) in the past 5 years ( approximate $USD )?\"\n",
    "df.ML_spent.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1b237e0-a900-4f06-b03f-aa789aa5f879",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recode: no spending=0, little ($1-$99 and $100-$999) = 1, spending ($1000-$9,999, $1000-$9,999, and more)=2\n",
    "df['ML_spent'] = df['ML_spent'].replace(['$0 ($USD)'],'0')\n",
    "df['ML_spent'] = df['ML_spent'].replace(['$1-$99'],'1')\n",
    "df['ML_spent'] = df['ML_spent'].replace(['$100-$999'],'1')\n",
    "df['ML_spent'] = df['ML_spent'].replace(['$1000-$9,999'],'2')\n",
    "df['ML_spent'] = df['ML_spent'].replace(['$10,000-$99,999'],'2')\n",
    "df['ML_spent'] = df['ML_spent'].replace(['$100,000 or more ($USD)'],'2')\n",
    "df.ML_spent.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d4160b1-e773-4873-9f35-ac0d6e510bb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "### FEATURE ENGINEERING: DATA TOOLS USED IN DATA ANALYSIS\n",
    "### SURVEY QUESTION: \"What is the primary tool that you use at work or school to analyze data? (Include text response)\"\n",
    "df.data_tool.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b9eacec-7eeb-4aa0-ac61-7a76c96a47c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Since I am more interested in the data science datatools that are in listed in the first group, I will regroup this variable by introducing only two categories.\n",
    "df['data_tool'] = df['data_tool'].replace(['Local development environments (RStudio, JupyterLab, etc.)'],'1')\n",
    "df['data_tool'] = df['data_tool'].replace(['Basic statistical software (Microsoft Excel, Google Sheets, etc.)'],'0')\n",
    "df['data_tool'] = df['data_tool'].replace(['Business intelligence software (Salesforce, Tableau, Spotfire, etc.)'],'0')\n",
    "df['data_tool'] = df['data_tool'].replace(['Cloud-based data software & APIs (AWS, GCP, Azure, etc.)'],'0')\n",
    "df['data_tool'] = df['data_tool'].replace(['Advanced statistical software (SPSS, SAS, etc.)'],'0')\n",
    "df['data_tool'] = df['data_tool'].replace(['Other'],'0')\n",
    "df.data_tool.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "daa49d95-2343-44ac-9236-41a1e3c38671",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.iloc[:, [0,1,2,4,5,6,7,8,9,10,11,12,13,3]]\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "45435c11-2bce-4126-bce0-e6ce9bd5b460",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5670 entries, 1 to 20035\n",
      "Data columns (total 14 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   age         5670 non-null   float64\n",
      " 1   gender      5670 non-null   float64\n",
      " 2   educ        5670 non-null   float64\n",
      " 3   experience  5618 non-null   float64\n",
      " 4   Python_or   5670 non-null   float64\n",
      " 5   ML_exp      5167 non-null   float64\n",
      " 6   comp_size   5226 non-null   float64\n",
      " 7   team        5177 non-null   float64\n",
      " 8   ML_buss     5108 non-null   float64\n",
      " 9   salary      4945 non-null   float64\n",
      " 10  ML_spent    4876 non-null   float64\n",
      " 11  data_tool   4443 non-null   float64\n",
      " 12  region      5670 non-null   float64\n",
      " 13  title       5670 non-null   float64\n",
      "dtypes: float64(14)\n",
      "memory usage: 664.5 KB\n"
     ]
    }
   ],
   "source": [
    "df = df.astype('float')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bfb8fe2d-3690-430c-9728-9ac1f5a69c66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Esu83MDCQX/7lX+5FaZUQmgMAAACVsDxLPSzPAjTlVa96Va655pocPnz4uAd9zt2OHDmSsiyPu258fDx79+5tqOqTE5oDAAAAlbA8S/UszwI06YwzzsiVV155wnM6nU5GR0ePC9K/9rWv5Y477qipyuUTmgMAAAAAsGyTk5PZsWNHnnzyyUXPKcsy7XY74+PjmZiYyPj4+AnPXw2E5gCwjvmT6Xr4k2kA1iu/NerhtwbQlFtvvTVf/OIXmy6j54TmALCO+ZPp6vmTaQDWM781que3BqxP3//+93P48OEVXdvtdtPpdNLtdhfc5o6VZfmsczudTsqynN0///zzc+TIkdnzOp1OxsfH0263T1jHhg2rN5pevZUBAAAAAHCc7373u/nQhz7UdBmn7GShepOE5gAAAAAAp4lLLrkkv/Ebv5Gf/OQnzxory3J2vyiKJY3NPT53bP7xuecvNDN9sVnpC81aHxkZydTU1PK/fE2E5gAAAAAAp4mNGzfmXe96V9NlnJIdO3Zk586dTZexqL6mCwAAAAAAgNXCTHMAAAAAgHVo7gM9V7pNTU1lYmIiExMTGR8ff9b+Qq8PP/xw01/9hITmAAAArDuDg4MZGhpquox1QZ+rNzw83HQJQI0eeeSRvO1tb2u6jBUbGBjIGWeckSuvvLLpUhYlNAcAAGDdGR4ezrZt25ouY03bvn17kuhzxWb6DKwfq/kBmksxPj6e8fHx3Hrrrbn66qubLmdBQnMAAAAAgNPE+eefn127di04tnv37lxzzTU1V7QyR44cabqERXkQKAAAAADAGnD++ec3XcKaYKY5AEDFrJtbH32uhz7XQ5+rp8f10OfqWdMcmLF///6mS1hUURTZvHlzNm3alE2bNuXVr3510yUtSmgOAFAx6+ZWz7q59dDneuhz9fS4HvpcD2uaA3Ndcskl+cpXvpKjR49mampqdpuYmMj4+PgJXycmJtJut9PpdNJut5+1f7L3C411u93Z2sqynF3PPEluvPHGXHXVVU216oSE5gAAAAAAp5Hdu3fn4MGDK75+06ZN2bhxY57znOek2+3ObjNB92LbzHhZls86d6GxdrudqampTE5OZnJycnb/ySefzNGjR3vYkd4SmgPAOmbZkProcz30uR76XA99rp4e10Ofq2d5Flhfbrvttnz4wx9uuow1TWgOAOuYZUOq50/T66HP9dDneuhz9fS4HvpcD8uzwPqzdevWvP3tb8+jjz76rLGyLJMcWz/8VM29V1mWKctydkb5zP7JZqcvNnbo0KGMjY2dco1VEZoDAAAAAJwm+vv78453vKPpMk7Jjh07snPnzqbLWFRf0wUAAAAAAMBqYaY5AAAAAAALmr/USqfTOW7ZlZUcX2hpmdVEaA4AAAAAsAY89NBDp/3SLauB5VkAAAAAANaAn/zkJ02XsCaYaQ4AULHBwcEMDQ01Xca6oM/10Od66HP19Lge+ly94eHhpksAanTw4MG8853vTKvVarqUNUtoDgDrmDAXAADg9PLYY48JzCsmNAeAdWx4eDjbtm1ruow1bfv27UmizxXT53rocz30uXp6XA99rsdMn4H14+KLL86uXbsWHCvLcvaBm/MfyFmW5XEP5FzK2Mz95o8tdI/ljH3ve9/LPffcU3Pnlk5oDgAAAACwBnS73XzhC1/IY489tqLry7JMkhRFseD+zGfMDcPn78+8P9HxgwcPnvqXrZDQHACgYpbBqY8+10Of66HP1dPjeuhz9axpDsy47bbb8pd/+ZdNl3HaE5oDAFTMMjjVswRAPfS5HvpcPT2uhz7Xw/IswFyXXXZZrrvuuhw+fHhF189d3mX+jPGVzChf6F6dTicPP/xwnn766d5++R4SmgMAAAAArAFFUeRVr3pV02Wc1I4dO7Jz586my1hUX9MFAAAAAADAaiE0BwAAAACAaUJzAAAAAACYJjQHAAAAAIBpQnMAAAAAAGrR6XQyOTnZdBkntKHpAgAAAAAAWFseeeSRvO1tb1t0fMOG1RtNr97KAAAAgNPa4OBghoaGmi5jzRseHm66BGCVKcsy7XY77XY7k5OTmZqamt1vt9uZmppa0v6pbA8++OAJa2y32/U0YwWE5gAAAKuMoLEeegzAWvP9738/H/zgB3P06NGmSzmpN7zhDU2XsCihOQAAwCozPDycbdu2NV3GmrV9+/Yk0eOK6XM9ZvoMkCTnnXde3vzmN2d8fDzdbjfdbjedTmf2dWZ/7vH5Y0s9fqJ7zj2+mFtvvTUf/OAHa+zO0gnNAQAAAADWgOc///n57d/+7abLOM7cMH1sbCyHDh3KF77whezatavp0hYlNAcAAAAAoKfuv//+/M7v/E7TZayI0BwAAACohPX56+FBoMBqNDY21nQJKyY0BwAAACphff7qWdMcWK22bNmSf/zHf8zo6GgOHz6cRx99NPv378/+/fuzZ8+ePPnkk02XuCihOQAAAAAAp6zT6WR8fDwTExO55ZZb8pnPfKbpklZEaA4AAAAAsA51u92UZTn7oM6Zbeb9YmNlWabb7ebOO+88bYPxExGaAwAAAACcJn784x/nne98Z9NlrGl9TRcAAAAAAMDSrOa1wNcKoTkAAAAAwGniBS94QdMlrHmWZwEAAFhlBgcHMzQ01HQZa54e10Ofqzc8PNx0CUCNzj///Ozatatn95tZn3yxNc2Hh4fz8Y9/vGefN+PKK6/s+T17RWgOAACwygwPD2fbtm1Nl7Fmbd++PUn0uGL6XI+ZPgOsVFEU6e/vT39//4Ljr3nNa7Jr166UZZmxsbG0Wq3ZbWRkJCMjI5mcnMz4+HgmJiYyNjaWiYmJ2fdHjx7NU089lccffzydTmf2vjfffHOuueaaur7msgjNAQAAAAA4oaIocuaZZ+bMM8/Mi1/84pOef/vtt+cDH/jAouNTU1O9LK+nrGkOAAAAAEBPPfXUU02XsGJCcwAAAAAAeuplL3tZ0yWsmOVZAAAAAABOIwcOHMjRo0efdbwsyyTHllKZ/3DPhR70udKxmYeHzj+30+kcN/bmN7/5uPHJyckcPnw4+/btS6vVqrttSyY0BwAAAAA4Tdxxxx35gz/4g6bLWNOE5gAAAAAAp4mXv/zlueKKK7J///5Fz2m32zly5EharVba7XaN1a0NQnMAANaEwcHBDA0NNV3GuqDP9dDn6ulxPfS5esPDw02XANTojDPOyLXXXrvg2A9+8IO85z3vqbmipdm4cePstmnTplx++eVNl7QooTkAAGvC8PBwtm3b1nQZa9r27duTRJ8rps/V0+N66HM9ZvoMkCQvfOEL87znPW92vfCBgYGcddZZOeuss3L22WfnjDPOSFEUSXLc2uNz1yaf2V/s+Pyxhc5ZyNTUVKampmbff/3rX89VV11VfVNWQGgOAAAAALAGvOAFL8jf//3fN1rDTBh/77335r3vfe+i542NjdVY1fL0NV0AAAAAAABrQ1EU6e/vz4YNp+98baE5AAAAAAA99cgjjzRdwoqdvnE/AAAAAAC1KcsynU5ndn3yE20bNmzI5s2bMzExseC9Nm7cWHP1Syc0BwAAAABgQXv27MnVV1/d8/vOfSjoamN5FgAAAAAAFnTeeedlYGCg5/et4p69YqY5AAAAAAALetGLXpSvf/3rp3yfTqeTycnJtNvtfO5zn8s//MM/9KC6agjNAQAAAACoVH9/f84444wkq3uWeWJ5FgAAAAAAmCU0BwAAAACAaZZnAQAAAACgMuPj4xkZGUmr1Uqr1crDDz/cdEknJDQHAAAAAKCn7rrrrrzvfe9ruowVEZoDAAAAALBs3W43N910U0ZGRpIkZVnOju3bt6+psk6Z0BwA1rHBwcEMDQ01XcaaNzw83HQJANAIvzXq4bcG0JSbbropn/zkJ5suo+eE5gCwjg0PD2fbtm1Nl7Gmbd++vekSAKAxfmtUz28NoEm/+qu/mlarlUOHDiU5fqb5gQMH8u1vf3vRa/v6+iqvb6WE5gAAAEAlzDSvh5nmQFP6+/vzlre8ZcGx22+//YShebfbraqsUyY0BwAAACphpnn1zDQHVqt2u910CSu2eufAAwAAAABwWjr//PNzzjnnLDo+MDBQYzXLY6Y5AAAAAAA9de655+bLX/7ygmOf/exn83d/93c1V7R0QnMAAAAAAHqqLMs8+OCDOXz4cFqt1nHbXXfd1XR5JyQ0BwAAACrhQaD18CBQYDX60pe+lM985jOLjr/mNa+psZrlEZoDAAAAAKwBhw4dyu///u/n4YcfbrqUk7r11lubLmFRQnMAgIqZZVcffa6HPtdDn6unxwCsNfv37z8tAvPVTmgOAFCx4eHhbNu2reky1rTt27cniT5XTJ/roc/V0+N66HM9ZvoMkCSveMUrsmvXriRJt9tNt9tNp9OZfZ3Zr/P4/LFOp5M777wzP/zhDxvu1uKE5gAAAAAAa0xfX1/6+vqyYcOxCLjb7eaGG27I008/fUr3nQnAT7bNDcvnb4899lgvvmJlhOYAsI5ZNqQeHs7FWuL/G/XR5+rpcT30uXp+awBL8a1vfctfpyyR0BwA1jHLhlTPj1LWGv/fqJ4lLaqnx/XQ53r4rQEs1eWXX56rr746Bw8eXNZ1Bw4cyE033VRRVauT0BwAAGCVMaO/HnpcD32unpnmwFL09fXlTW9607Kvu+WWW9ZdaN7XdAEAAAAAALBaCM0BAAAAAFjQ5s2bmy6hdpZnAQAAWGWsHV8ta23XQ5/rYU1zoGqXXXZZvvKVr2RqaiplWWZqaupZW7vdzuTk5IL7C51/991354EHHmj6qy1KaA4AAAAAwKJ+6qd+atnXlGWZ73znOzly5EjKsswzzzyTVquV8fHxTExM9L7IHhKaAwAAAJXwUNt6eBAoULVHHnkk4+Pjs+87nc6iM8tn9m+44YZVPZv8RITmAAAAQCUsNVQ9y7MAVfvWt76Vj370o02XUSsPAgUAAAAAYEEXXHBB0yXUzkxzAACAVcaSFvXQ43roc/UszwJU6cEHH2y6hNoJzQEAAFYZS1pUa2Y5Cz2ulj7Xw/IswFydTidf+tKX8vjjj6/o+rIskyRFURy3//rXvz7tdjtTU1MZGxvLxMREJiYmZh/qObM/d93zk9mwYfVG06u3MgCANcKM0frocz30uR76XD09roc+V89Mc2DGbbfdlj//8z9vuowlabfbTZewKKE5AEDFzBitntmM9dDneuhz9fS4HvpcDzPNgbkuu+yyXHvttTl48OCSr5k/q7wsy5RlmW63m263m06nk06nc9z75RxfaOzAgQPLqrFuQnMAAAAAgDWgKIpcccUVTZdxUjt27MjOnTubLmNRfU0XAAAAAAAAq4XQHAAAAAAApgnNAQAAAABgmtAcAAAAAACmCc0BAAAAAGDahqYLAAAAAACgN8bGxtJut1MURTqdTjqdTrrdbrrd7ux+1cfnj80//v3vf7/pNp2Q0BwAAAAAYA3Ys2dPrr766qbLOKnnPOc52bJlS9NlLEpoDgDr2ODgYIaGhpouY80bHh5uugQAAGAduPDCC7Nly5Y88MADs8fKskxZlrP7c4/PH1/oeLfb7XmdzzzzTB566KGe37dXhOYAsI4NDw9n27ZtTZexpm3fvr3pEoDTkH/UrIce10Ofq+cf6IEZZ599dj71qU/19J7f/OY387GPfayn90ySgwcP9vyevSI0BwAAWGX8o2a1Zv5BU4+rpc/18A/0wKkqyzJTU1Npt9uZmprK5OTkcfsvfOEL8/73vz8jIyOzx8fHx3P06NGMjY3l6NGjs/vPPPPM7LGJiYkTfu6GDas3ml69lQEAAKxTZprXQ4/roc/VM9McmHHgwIHs2LEjY2NjmZqaWnBrt9uZnJw8Liivy4YNG7J58+YMDAzkl37pl2r73OUSmgMAVEz4VR99roc+AwCsTmNjY3n00UczMTGRbrebTqeTTqeTbrc7+37mtSzLFEWRDRs2zL6vWrvdTrvdzjPPPJPdu3dX/nkrJTQHAKiYZRaqZwmAeuhzPfS5enpcD32uh+VZgLkuuOCC/MVf/MWKrp156OfcoH1+4N6r4zfffLPQHAAAAACA1asoivT396e/v7/yz9q3b9+qDs37mi4AAAAAAABWC6E5AAAAAABME5oDAAAAAMA0a5oDAAAAAKwDBw8ezMTExOz7+Q//rGv7wQ9+0GAXTk5oDgAAAACwxu3evTvXXHNN02WcFoTmAAAAAABr3MUXX5ytW7dm//79PbtnWZYnHV9oJvvk5GT6+/t7VkevCc0BAAAAANa4s846K5/85CebLiNJsmPHjuzcubPpMhblQaAAAAAAADBNaA4AAAAAANMszwIAAAAAQM899thjGR0dzcjISEZGRtJqtdJqtbJnz56mSzuhJYXmRVH8epLXJBlM8ookZyf5fFmWbz3BNa9M8pEkv5xkIMn9Sf4qyb8vy7KzyDVvT/LuJBcn6SS5K8mflGX51UXOPyPJB5P8ZpJ/mmQkyVCSj5Vled9SvhsAAAAAAL11yy235Lrrrmu6jBVZ6kzzj+RYWD6a5JEk/92JTi6K4l8m+VKS8SR/m+Rgkv8hyZ8luSzJv1rgmj9JcvX0/Xck2ZRjYfgNRVFcVZblp+edvznJN6bvd0eS/zPJfzt97zcWRXFFWZbfW+L3AwAAAABY87rdbrrdbjqdzuzrzH4vj7darRPWsXHjxlP6HkVRvDrJ+5NckuQlSf5NWZafW+Tczyb5nSR/UJbln5zs3ksNzf+XHAuz78+xGee7TlDsc3Ms9O4keW1ZlndMH782yS1Jfr0oit8sy/Jv5lzzyhwLzPcl+edlWR6aPv7vktyZ5E+KovhqWZYPzvmo9+VYYP7FJG8py7I7fc3fJvn7JH9VFMWWmeMAAAAAAGvZvffem3e/+91Nl7EkL3rRi071FmcluSfJf57eFjS9iso/T/KTpd54SaF5WZazIXlRFCc7/deTvCDJf54JzKfvMV4UxUeS3JzkXUn+Zs41/9P06/82E5hPX/NgURSfSXJtkn+T5GPTNRRzrvnA3GC8LMv/UhTFt5JcnpME/AAAAEB1BgcHMzQ01HQZa97w8HDTJQCrxE//9E+nr68v3e7qn0fc39+f2267LVu3bk1/f/+yry/L8r8m+a9JUhTF5xY6pyiKf5pjK5T8SpKvL/XeVTwI9Irp1xsXGPtmkqNJXlkUxeayLCeWcM3Xcyw0vyLToXmSf5bkZ5L8qCzLHy9yzeXT1wjNAQAAoAHDw8PZtm1b02Wsadu3b2+6BGAVGR8fPy0C8yR56KGHct111+Wiiy7K9ddfv6Lg/ESKotiQ5AtJ/m1ZlvctYTL4rL6eVnLMz02//mj+QFmW7SQ/zrGw/meTpCiK5yQ5L8loWZaPLnC/vdOvL1vKZ5zgGgAAAACANavT6TRdwrKMjY3l3nvvze7du6u4/R8lebosyz9f7oVFWZbLu6AoXptjs7c/X5blWxcY/1GSC5NcWJbl/QuMfzvJK5O8sizL24qieEmSA0kOlGX53yxw/sYkk0kmy7LcPH3sXyf5/Alq+NUkNyW5qSzLNyzrCwIAAAAA0HOve93rrk3y8Rw/mbub5GO7du36tyu9b1EUo0neM/Mg0KIoXpPk/04yWJblk9PHHkzy6V4+CLSXZubBLy+tX975K/0MAAAAAAAqsGvXruuSXFfDR70uyYuTPDpnWZb+JJ8simLbQpO356oiNG9Nvz5vkfHnzjvvZOc/b955K/kMAAAAAADWh/+Q5Ivzjv0/ObbG+Y6TXVxFaP7DJL+YY+uJ3zl3YHrx9fOTtJM8kCRlWT5TFMWBJOcVRfHiBdY1v3D6de765T+cfl1szfKFrgEAAAAAYA0oiuKsJBdMv+1L8jNFUQwmOViW5f4kT8w7fyrJY2VZ/jAnUcWDQG+Zfr1ygbFXJzkzyXfKspxY4jX//bxzkmRfkv1JXlYUxflLvAYAAAAAgLXhF5PcNb2dkWMP/rwryf96qjeuIjT/YpKnkvxmURS/OHOwKIqBJDOLuc9/Yul/nH79w6IozplzzUuTvDvJRJL/NHO8PPb00plrri+Kom/ONf8yyeVJ7k1yaw++DwAAAAAAq0hZlkNlWRYLbO9Y5PyXLuUhoElSHMufT3JSUfxakl+bfntukjfk2PIq35o+9lRZlu+fd/4Xk4wn+ZskB5P8iyQ/N338N8p5H1wUxf+R5H1JHpk+Z1OStyT5J0muKsvy0/PO35xjM8lfmeSOJDcn+Zkk/yrJZJIryrL83km/HAAAAAAATFtqaP7xJB87wSkPlWX50nnXXJbkD5NcmmQgyf1J/irJp8qy7CzyOW9P8p4kFyfpJtmT5N+VZfnVRc4/I8kHk/zrHAvMR5IMJflYWZb3nvSLAQAAAADAHEsKzQEAAAAAYD2oYk1zAAAAAAA4LQnNAQAAAABgmtAcAAAAAACmCc0BAAAAAGCa0BwAAAAAAKYJzQEAAAAAYJrQHAAAAAAApgnNAQAAAABgmtAcAAAAAACm/X8czjpmjLjC7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Examining the missing data\n",
    "msno.bar(df)\n",
    "filtered_data = msno.nullity_filter(df, filter='bottom', n=15, p=0.999) # or filter='top'\n",
    "msno.matrix(df.sample(1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "53c4d9b7-f2f0-4054-b25a-2a72819a5328",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.heatmap(df)\n",
    "### High correlations!\n",
    "msno.dendrogram(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8b5e32e3-c8f8-4dce-b11f-3ea6a6c5fb1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "### MISSING VALUES: ORDINAL ENCODING\n",
    "ordinal_enc_dict = {}\n",
    "# Loop over columns to encode\n",
    "for col_name in df:\n",
    "# Create ordinal encoder for the column\n",
    "    ordinal_enc_dict[col_name] = OrdinalEncoder()\n",
    "# Select the non-null values in the column\n",
    "    col = df[col_name]\n",
    "    col_not_null = col[col.notnull()]\n",
    "    reshaped_vals = col_not_null.values.reshape(-1, 1)\n",
    "# Encode the non-null values of the column\n",
    "    encoded_vals = ordinal_enc_dict[col_name].fit_transform(reshaped_vals)\n",
    "# Replace the values in the column with ordinal values\n",
    "    df.loc[col.notnull(), col_name] = np.squeeze(encoded_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "59ca8e92-c8cf-4adb-a560-e180c496ae74",
   "metadata": {},
   "outputs": [],
   "source": [
    "### IMPUTING WITH KNN\n",
    "data_KNN_imputed = df.copy(deep=True)\n",
    "from sklearn.impute import KNNImputer\n",
    "KNN_imputer=KNNImputer()\n",
    "data_KNN_imputed.iloc[:, :] = np.round(KNN_imputer.fit_transform(data_KNN_imputed))\n",
    "for col in data_KNN_imputed:\n",
    "    reshaped_col = data_KNN_imputed[col].values.reshape(-1, 1)\n",
    "    data_KNN_imputed[col] = ordinal_enc_dict[col].inverse_transform(reshaped_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "794981ea-6d0d-4432-a2f8-f662d9057b42",
   "metadata": {},
   "outputs": [],
   "source": [
    "### IMPUTING WITH MICE\n",
    "from fancyimpute import IterativeImputer\n",
    "mice_imputer = IterativeImputer()\n",
    "data_mice=df.copy(deep=True)\n",
    "data_mice.iloc[:,:] = np.round(mice_imputer.fit_transform(data_mice))\n",
    "for col in data_mice:\n",
    "    reshaped_col1 = data_mice[col].values.reshape(-1, 1)\n",
    "    data_mice[col] = ordinal_enc_dict[col].inverse_transform(reshaped_col1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7f4e1e7-fbac-410a-a4dc-551d322e87aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Compare the results\n",
    "import statsmodels.api as sm\n",
    "df_cc=df.dropna(how='any')\n",
    "#df_cc=df_cc.astype('category')\n",
    "X=sm.add_constant(df_cc.iloc[:, :-1])\n",
    "y=df_cc['title']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d95ae77c-5da4-40c7-9932-434d2e278f02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  title   R-squared:                       0.128\n",
      "Model:                            OLS   Adj. R-squared:                  0.125\n",
      "Method:                 Least Squares   F-statistic:                     49.07\n",
      "Date:                Tue, 17 May 2022   Prob (F-statistic):          5.22e-119\n",
      "Time:                        16:14:33   Log-Likelihood:                -2876.0\n",
      "No. Observations:                4374   AIC:                             5780.\n",
      "Df Residuals:                    4360   BIC:                             5869.\n",
      "Df Model:                          13                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0909      0.044      2.078      0.038       0.005       0.177\n",
      "age            0.0085      0.009      0.978      0.328      -0.009       0.026\n",
      "gender         0.0061      0.019      0.323      0.747      -0.031       0.043\n",
      "educ           0.0421      0.009      4.456      0.000       0.024       0.061\n",
      "experience    -0.0432      0.013     -3.222      0.001      -0.069      -0.017\n",
      "Python_or      0.0792      0.027      2.981      0.003       0.027       0.131\n",
      "ML_exp         0.0906      0.008     11.896      0.000       0.076       0.106\n",
      "comp_size      0.0057      0.011      0.539      0.590      -0.015       0.027\n",
      "team          -0.0150      0.011     -1.356      0.175      -0.037       0.007\n",
      "ML_buss        0.0400      0.006      6.690      0.000       0.028       0.052\n",
      "salary         0.0022      0.007      0.329      0.742      -0.011       0.016\n",
      "ML_spent       0.0076      0.010      0.786      0.432      -0.011       0.026\n",
      "data_tool      0.1118      0.015      7.640      0.000       0.083       0.140\n",
      "region        -0.0188      0.007     -2.801      0.005      -0.032      -0.006\n",
      "==============================================================================\n",
      "Omnibus:                    28084.483   Durbin-Watson:                   2.064\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              404.869\n",
      "Skew:                          -0.002   Prob(JB):                     1.21e-88\n",
      "Kurtosis:                       1.510   Cond. No.                         39.8\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm=sm.OLS(y, X).fit()\n",
    "print(lm.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3277da2-e4a2-4ed3-bf0d-86fa5467f343",
   "metadata": {},
   "outputs": [],
   "source": [
    "### KNN imputation\n",
    "X1= sm.add_constant(data_KNN_imputed.iloc[:, :-1])\n",
    "y1=data_KNN_imputed['title']\n",
    "lm_KNN=sm.OLS(y1,X1).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4a263811-8660-486a-b49a-64cd8aed4e37",
   "metadata": {},
   "outputs": [],
   "source": [
    "### MICE imputation\n",
    "X2= sm.add_constant(data_mice.iloc[:, :-1])\n",
    "y2=data_mice['title']\n",
    "lm_mice=sm.OLS(y2,X2).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "84229d30-d39f-48e1-8067-f131c6d786a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               Complete  KNN Imp.  MICE Imp.\n",
      "R_squared_adj  0.125034  0.125996   0.142947\n"
     ]
    }
   ],
   "source": [
    "print(pd.DataFrame({'Complete': lm.rsquared_adj,\n",
    "'KNN Imp.': lm_KNN.rsquared_adj,\n",
    "'MICE Imp.': lm_mice.rsquared_adj},\n",
    "index=['R_squared_adj']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f2760ec-3496-460c-a690-bdf240b23fc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'salary')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['salary'].plot(kind='kde', c='red', linewidth=3)\n",
    "data_KNN_imputed['salary'].plot(kind='kde')\n",
    "data_mice['salary'].plot(kind='kde')\n",
    "labels = ['Baseline (Complete Case)', 'KNN Imputation','MICE Imputation']\n",
    "plt.legend(labels)\n",
    "plt.xlabel('salary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "400de3ce-1330-47d5-9bbf-bcccf30b122f",
   "metadata": {},
   "outputs": [],
   "source": [
    "### MICE has the best performance in imputation. \n",
    "data=data_mice.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ac437f87-b1f4-4ac7-8bc0-a3897bcb7b34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5670 entries, 1 to 20035\n",
      "Data columns (total 14 columns):\n",
      " #   Column      Non-Null Count  Dtype   \n",
      "---  ------      --------------  -----   \n",
      " 0   age         5670 non-null   category\n",
      " 1   gender      5670 non-null   category\n",
      " 2   educ        5670 non-null   category\n",
      " 3   experience  5670 non-null   category\n",
      " 4   Python_or   5670 non-null   category\n",
      " 5   ML_exp      5670 non-null   category\n",
      " 6   comp_size   5670 non-null   category\n",
      " 7   team        5670 non-null   category\n",
      " 8   ML_buss     5670 non-null   category\n",
      " 9   salary      5670 non-null   category\n",
      " 10  ML_spent    5670 non-null   category\n",
      " 11  data_tool   5670 non-null   category\n",
      " 12  region      5670 non-null   category\n",
      " 13  title       5670 non-null   category\n",
      "dtypes: category(14)\n",
      "memory usage: 124.1 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "691e893d-0acb-4f20-8b49-6d95db554d56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### DATA VISUALIZATION\n",
    "data.title.value_counts().plot(kind=\"bar\", color=[\"salmon\", \"lightblue\"])\n",
    "### We have 2994 non-data scientists and 2676 data scientists, so our problem is balanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "02525b36-d1f6-47f7-98d4-99ddf9c06d3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "region  title\n",
       "0.0     0.0       494\n",
       "        1.0       392\n",
       "1.0     0.0       621\n",
       "        1.0       663\n",
       "2.0     0.0       548\n",
       "        1.0       648\n",
       "3.0     0.0      1331\n",
       "        1.0       973\n",
       "dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " data.groupby(['region','title']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "6f3a12f0-d1f4-48b9-92d1-73f822e04464",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1800 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25, 25))\n",
    "\n",
    "for i, column in enumerate(data, 1):\n",
    "    plt.subplot(5, 5, i)\n",
    "    data[data[\"title\"] == 0.0][column].hist(bins=15, color='blue', label='Non Data scientists', alpha=0.8)\n",
    "    data[data[\"title\"] == 1.0][column].hist(bins=25, color='salmon', label='Data Scientists', alpha=0.8)\n",
    "    plt.legend()\n",
    "    plt.xlabel(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "152429fb-8c8b-43a2-a59f-d0bb32e3ffac",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Observations from the above plot:20s=1, 30s=2, 40s=3, 50s=4, 60s&above=5\n",
    "### The size of age groups between data scientists and non-datascientists are almost evenly scattered. However, data scientists are more likely to be in their 30s and 40s.\n",
    "### The job titles are almost evenly represented by all sexes almost evenly. Though females are more likely to take non-data scientist positions rather than data scientist position.\n",
    "### 63% of the PhDs in the sample work as data scientists. Non-data scientists are more likely to be dominant in other education categories (other, BA, and MA).\n",
    "### Data scientists are more likely to be experienced coders(mid-level and senior coders) that non-data scientists.\n",
    "### Data scientists are slightly more likely to use Python in their daily jobs.\n",
    "### Data scientists tend to have more experienced in using Machine Learning algorithms (categories - mid, senior, and lead).\n",
    "### Data scientists are more likely to work in mid-size or big companies as well as mid-size or big teams.\n",
    "### Data scientists are more likely to work in companies where well-established ML methods are employed.\n",
    "### Both groups almost equally distributed in various salary categories. However, data scientists are more likely to earn high salaries.\n",
    "### Data scientists are more likely to work at companies that spend more money to the ML services ($1000-$9,999, $1000-$9,999, and more).\n",
    "### Data scientists are more likely to use work tools in local development environments (RStudio, JupyterLab).\n",
    "### Both groups almost equally distributed in various regions of the world; however, data scientists are more likely to be located in America and Europe compared to non-data scientists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "29b1e71b-8c7b-4aed-8d00-605636d60e02",
   "metadata": {},
   "outputs": [],
   "source": [
    "### END OF DATA PREPARATION\n",
    "data.to_pickle(\"data_readyforanalysis.pkl\")"
   ]
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}