diff --git a/your-project/Finance.ipynb b/your-project/Finance.ipynb
new file mode 100644
index 0000000..2ef4905
--- /dev/null
+++ b/your-project/Finance.ipynb
@@ -0,0 +1,1718 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import scipy.stats as st\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from scipy.stats import f_oneway\n",
+    "from statsmodels.stats.multicomp import pairwise_tukeyhsd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Cleaning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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",
+       "        vertical-align: top;\n",
+       "    }\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>Carimbo de data/hora</th>\n",
+       "      <th>01 / 13 - Gender</th>\n",
+       "      <th>02 / 13 - Whats your age? (only number)</th>\n",
+       "      <th>03 / 13 - Marital Status:</th>\n",
+       "      <th>04 / 13 - Education Level</th>\n",
+       "      <th>05 / 13 - Monthly Income (x min wage)</th>\n",
+       "      <th>06 / 13 - Importance of Financial Education</th>\n",
+       "      <th>07 /13 - Source of Financial Education</th>\n",
+       "      <th>08 / 13 - Level of Knowledge in the Stock Market</th>\n",
+       "      <th>09 / 13 - Financial Planning and Control</th>\n",
+       "      <th>10 / 13 - Percentage of Salary Invested</th>\n",
+       "      <th>11 / 13 - Types of Investments</th>\n",
+       "      <th>12 / 13 - Investment Objectives</th>\n",
+       "      <th>13 / 13 - What´s your acceptance of risk in invested capital</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2023/08/13 2:53:39 PM GMT+1</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>39 to 55</td>\n",
+       "      <td>Married or Living together</td>\n",
+       "      <td>Postgraduate Degree</td>\n",
+       "      <td>Up to 1 minimum wage</td>\n",
+       "      <td>Very important</td>\n",
+       "      <td>Family and friends</td>\n",
+       "      <td>Medium</td>\n",
+       "      <td>Medium (I define my expenses in a basic way)</td>\n",
+       "      <td>to 10%</td>\n",
+       "      <td>Savings account</td>\n",
+       "      <td>Retirement</td>\n",
+       "      <td>Preserve capital with no risk</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2023/08/13 2:54:47 PM GMT+1</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>39 to 55</td>\n",
+       "      <td>Married or Living together</td>\n",
+       "      <td>Postgraduate Degree</td>\n",
+       "      <td>Above 5 minimum wages</td>\n",
+       "      <td>Very important</td>\n",
+       "      <td>Self-taught</td>\n",
+       "      <td>High</td>\n",
+       "      <td>Medium (I define my expenses in a basic way)</td>\n",
+       "      <td>to 30%</td>\n",
+       "      <td>Stocks</td>\n",
+       "      <td>Retirement</td>\n",
+       "      <td>Earn more with low risk</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2023/08/14 2:35:01 PM GMT+1</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>24</td>\n",
+       "      <td>Married or Living together</td>\n",
+       "      <td>Secondary School</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Very important</td>\n",
+       "      <td>Self-taught</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Medium (I define my expenses in a basic way)</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Retirement;Emergencies</td>\n",
+       "      <td>Earn more with low risk</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2023/08/14 2:35:35 PM GMT+1</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>32</td>\n",
+       "      <td>Single or Living alone</td>\n",
+       "      <td>Bachelor´s Degree</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Slightly important</td>\n",
+       "      <td>Self-taught</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Medium (I define my expenses in a basic way)</td>\n",
+       "      <td>to 10%</td>\n",
+       "      <td>Real estate;crypto</td>\n",
+       "      <td>Retirement;Children future</td>\n",
+       "      <td>Not afraid to take risks</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023/08/14 2:36:08 PM GMT+1</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>23</td>\n",
+       "      <td>Single or Living alone</td>\n",
+       "      <td>Bachelor´s Degree</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Very important</td>\n",
+       "      <td>Family and friends</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Medium (I define my expenses in a basic way)</td>\n",
+       "      <td>to 10%</td>\n",
+       "      <td>Savings account</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>Earn more with low risk</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Carimbo de data/hora 01 / 13 - Gender  \\\n",
+       "0  2023/08/13 2:53:39 PM GMT+1           Female   \n",
+       "1  2023/08/13 2:54:47 PM GMT+1             Male   \n",
+       "2  2023/08/14 2:35:01 PM GMT+1             Male   \n",
+       "3  2023/08/14 2:35:35 PM GMT+1             Male   \n",
+       "4  2023/08/14 2:36:08 PM GMT+1           Female   \n",
+       "\n",
+       "  02 / 13 - Whats your age? (only number)   03 / 13 - Marital Status:  \\\n",
+       "0                                39 to 55  Married or Living together   \n",
+       "1                                39 to 55  Married or Living together   \n",
+       "2                                      24  Married or Living together   \n",
+       "3                                      32      Single or Living alone   \n",
+       "4                                      23      Single or Living alone   \n",
+       "\n",
+       "  04 / 13 - Education Level 05 / 13 - Monthly Income (x min wage)  \\\n",
+       "0       Postgraduate Degree                  Up to 1 minimum wage   \n",
+       "1       Postgraduate Degree                 Above 5 minimum wages   \n",
+       "2          Secondary School                                     1   \n",
+       "3         Bachelor´s Degree                                     3   \n",
+       "4         Bachelor´s Degree                                     1   \n",
+       "\n",
+       "  06 / 13 - Importance of Financial Education  \\\n",
+       "0                              Very important   \n",
+       "1                              Very important   \n",
+       "2                              Very important   \n",
+       "3                          Slightly important   \n",
+       "4                              Very important   \n",
+       "\n",
+       "  07 /13 - Source of Financial Education  \\\n",
+       "0                     Family and friends   \n",
+       "1                            Self-taught   \n",
+       "2                            Self-taught   \n",
+       "3                            Self-taught   \n",
+       "4                     Family and friends   \n",
+       "\n",
+       "  08 / 13 - Level of Knowledge in the Stock Market  \\\n",
+       "0                                           Medium   \n",
+       "1                                             High   \n",
+       "2                                                1   \n",
+       "3                                                1   \n",
+       "4                                                0   \n",
+       "\n",
+       "       09 / 13 - Financial Planning and Control  \\\n",
+       "0  Medium (I define my expenses in a basic way)   \n",
+       "1  Medium (I define my expenses in a basic way)   \n",
+       "2  Medium (I define my expenses in a basic way)   \n",
+       "3  Medium (I define my expenses in a basic way)   \n",
+       "4  Medium (I define my expenses in a basic way)   \n",
+       "\n",
+       "  10 / 13 - Percentage of Salary Invested 11 / 13 - Types of Investments  \\\n",
+       "0                                  to 10%                Savings account   \n",
+       "1                                  to 30%                         Stocks   \n",
+       "2                                     NaN                            NaN   \n",
+       "3                                  to 10%             Real estate;crypto   \n",
+       "4                                  to 10%                Savings account   \n",
+       "\n",
+       "  12 / 13 - Investment Objectives  \\\n",
+       "0                      Retirement   \n",
+       "1                      Retirement   \n",
+       "2          Retirement;Emergencies   \n",
+       "3      Retirement;Children future   \n",
+       "4                           Other   \n",
+       "\n",
+       "  13 / 13 - What´s your acceptance of risk in invested capital  \n",
+       "0                      Preserve capital with no risk            \n",
+       "1                            Earn more with low risk            \n",
+       "2                            Earn more with low risk            \n",
+       "3                           Not afraid to take risks            \n",
+       "4                            Earn more with low risk            "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "finance = pd.read_csv(\"Financial Research Questionnaire.csv\")\n",
+    "finance.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Drop the data columm\n",
+    "\n",
+    "finance = finance.drop('Carimbo de data/hora',axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop the first two test rows\n",
+    "\n",
+    "finance = finance.drop([0, 1]).reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Rename the columns\n",
+    "\n",
+    "new_columns = {'01 / 13 - Gender':\"Gender\", '02 / 13 - Whats your age? (only number)':\"Age\",\n",
+    "       '03 / 13 - Marital Status:':\"Marital\", '04 / 13 - Education Level':\"Education\",\n",
+    "       '05 / 13 - Monthly Income (x min wage)':\"Salary\",\n",
+    "       '06 / 13 - Importance of Financial Education':\"Importance\",\n",
+    "       '07 /13 - Source of Financial Education':\"Source\",\n",
+    "       '08 / 13 - Level of Knowledge in the Stock Market':\"Knowledge\",\n",
+    "       '09 / 13 - Financial Planning and Control':\"Control\",\n",
+    "       '10 / 13 - Percentage of Salary Invested':\"Percentage\",\n",
+    "       '11 / 13 - Types of Investments':\"Investments\", '12 / 13 - Investment Objectives':\"Objectives\",\n",
+    "       '13 / 13 - What´s your acceptance of risk in invested capital':\"Risk\"}\n",
+    "finance.rename(columns=new_columns, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Gender', 'Age', 'Marital', 'Education', 'Salary', 'Importance',\n",
+       "       'Source', 'Knowledge', 'Control', 'Percentage', 'Investments',\n",
+       "       'Objectives', 'Risk'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "finance.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Gender\n",
+       "Male      36\n",
+       "Female    22\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "finance[\"Gender\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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",
+       "        vertical-align: top;\n",
+       "    }\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>Gender</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Marital</th>\n",
+       "      <th>Education</th>\n",
+       "      <th>Salary</th>\n",
+       "      <th>Importance</th>\n",
+       "      <th>Source</th>\n",
+       "      <th>Knowledge</th>\n",
+       "      <th>Control</th>\n",
+       "      <th>Percentage</th>\n",
+       "      <th>Investments</th>\n",
+       "      <th>Objectives</th>\n",
+       "      <th>Risk</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>24</td>\n",
+       "      <td>Married or Living together</td>\n",
+       "      <td>Secondary School</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Very important</td>\n",
+       "      <td>Self-taught</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Medium (I define my expenses in a basic way)</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Retirement;Emergencies</td>\n",
+       "      <td>Earn more with low risk</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>32</td>\n",
+       "      <td>Single or Living alone</td>\n",
+       "      <td>Bachelor´s Degree</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Slightly important</td>\n",
+       "      <td>Self-taught</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Medium (I define my expenses in a basic way)</td>\n",
+       "      <td>to 10%</td>\n",
+       "      <td>Real estate;crypto</td>\n",
+       "      <td>Retirement;Children future</td>\n",
+       "      <td>Not afraid to take risks</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>23</td>\n",
+       "      <td>Single or Living alone</td>\n",
+       "      <td>Bachelor´s Degree</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Very important</td>\n",
+       "      <td>Family and friends</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Medium (I define my expenses in a basic way)</td>\n",
+       "      <td>to 10%</td>\n",
+       "      <td>Savings account</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>Earn more with low risk</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>39</td>\n",
+       "      <td>Married or Living together</td>\n",
+       "      <td>Master or plus</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Very important</td>\n",
+       "      <td>Family and friends</td>\n",
+       "      <td>2</td>\n",
+       "      <td>High (I categorize all my expenses)</td>\n",
+       "      <td>to 10%</td>\n",
+       "      <td>Stocks;Savings account</td>\n",
+       "      <td>Retirement;Children future;Emergencies;Unemplo...</td>\n",
+       "      <td>Preserve capital with no risk</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>23</td>\n",
+       "      <td>Single or Living alone</td>\n",
+       "      <td>Secondary School</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Very important</td>\n",
+       "      <td>Family and friends</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Medium (I define my expenses in a basic way)</td>\n",
+       "      <td>to 10%</td>\n",
+       "      <td>Savings account</td>\n",
+       "      <td>Retirement;Children future;Emergencies;Unemplo...</td>\n",
+       "      <td>Earn more with low risk</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Gender Age                     Marital          Education Salary  \\\n",
+       "0    Male  24  Married or Living together   Secondary School      1   \n",
+       "1    Male  32      Single or Living alone  Bachelor´s Degree      3   \n",
+       "2  Female  23      Single or Living alone  Bachelor´s Degree      1   \n",
+       "3    Male  39  Married or Living together     Master or plus      1   \n",
+       "4    Male  23      Single or Living alone   Secondary School      2   \n",
+       "\n",
+       "           Importance              Source Knowledge  \\\n",
+       "0      Very important         Self-taught         1   \n",
+       "1  Slightly important         Self-taught         1   \n",
+       "2      Very important  Family and friends         0   \n",
+       "3      Very important  Family and friends         2   \n",
+       "4      Very important  Family and friends         2   \n",
+       "\n",
+       "                                        Control Percentage  \\\n",
+       "0  Medium (I define my expenses in a basic way)        NaN   \n",
+       "1  Medium (I define my expenses in a basic way)     to 10%   \n",
+       "2  Medium (I define my expenses in a basic way)     to 10%   \n",
+       "3           High (I categorize all my expenses)     to 10%   \n",
+       "4  Medium (I define my expenses in a basic way)     to 10%   \n",
+       "\n",
+       "              Investments                                         Objectives  \\\n",
+       "0                     NaN                             Retirement;Emergencies   \n",
+       "1      Real estate;crypto                         Retirement;Children future   \n",
+       "2         Savings account                                              Other   \n",
+       "3  Stocks;Savings account  Retirement;Children future;Emergencies;Unemplo...   \n",
+       "4         Savings account  Retirement;Children future;Emergencies;Unemplo...   \n",
+       "\n",
+       "                            Risk  \n",
+       "0        Earn more with low risk  \n",
+       "1       Not afraid to take risks  \n",
+       "2        Earn more with low risk  \n",
+       "3  Preserve capital with no risk  \n",
+       "4        Earn more with low risk  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "finance.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['24', '32', '23', '39', '35', '16', '43', '17', '30', '44', '22',\n",
+       "       '29', '42', '45', '34', '27', '25', '31', '52', '75', '47', '73',\n",
+       "       '63', '78', '46', '40', '41', '50', '55', '33', '36', '28', '37'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Clean the ages with text\n",
+    "\n",
+    "finance['Age'] = finance['Age'].str.extract(r'(\\d+)', expand=False)\n",
+    "finance[\"Age\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([nan, 'Real estate;crypto', 'Savings account',\n",
+       "       'Stocks;Savings account', 'Real estate;Savings account',\n",
+       "       'Stocks;Real estate;Savings account',\n",
+       "       'Real estate;Savings account;Crypto', 'Real estate',\n",
+       "       'private pension', 'Stocks', 'Stocks;Real estate'], dtype=object)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "finance[\"Investments\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Married or Living together', 'Single or Living alone'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "finance[\"Marital\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([nan, 'to 10%', 'to 30% or more', 'to 20%'], dtype=object)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "finance[\"Percentage\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# GOODNESS OF FIT AND ASSOCIATION TESTING"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Invest x Marital"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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",
+       "        vertical-align: top;\n",
+       "    }\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>Marital</th>\n",
+       "      <th>Married or Living together</th>\n",
+       "      <th>Single or Living alone</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Percentage</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>to 10%</th>\n",
+       "      <td>14</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>to 20%</th>\n",
+       "      <td>7</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>to 30% or more</th>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Marital         Married or Living together  Single or Living alone\n",
+       "Percentage                                                        \n",
+       "to 10%                                  14                       6\n",
+       "to 20%                                   7                       7\n",
+       "to 30% or more                           7                       4"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Marital / Percentage\n",
+    "\n",
+    "#finance.pivot_table(index = \"Marital\", columns = \"Percentage\", aggfunc=\"count\").reset_index().T\n",
+    "marital_pivot = finance.pivot_table(index='Percentage', columns='Marital', aggfunc='size', fill_value=0)\n",
+    "marital_pivot\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[14, 6], [7, 7], [7, 4]]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "resultado_marital = marital_pivot.values.tolist()\n",
+    "resultado_marital"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Chi2ContingencyResult(statistic=1.4137700534759357, pvalue=0.49317804369524354, dof=2, expected_freq=array([[12.44444444,  7.55555556],\n",
+       "       [ 8.71111111,  5.28888889],\n",
+       "       [ 6.84444444,  4.15555556]]))"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## H0: the marital status are independent of the investment\n",
+    "## H1: the marital status are dependent of the investment\n",
+    "\n",
+    "\n",
+    "alpha = 0.05\n",
+    "st.chi2_contingency(resultado_marital)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x17b75bf6b30>,\n",
+       " <matplotlib.lines.Line2D at 0x17b75bf6ad0>]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(marital_pivot)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Percentage x Gender"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "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",
+       "        vertical-align: top;\n",
+       "    }\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>Gender</th>\n",
+       "      <th>Female</th>\n",
+       "      <th>Male</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Percentage</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>to 10%</th>\n",
+       "      <td>4</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>to 20%</th>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>to 30% or more</th>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Gender          Female  Male\n",
+       "Percentage                  \n",
+       "to 10%               4    16\n",
+       "to 20%               9     5\n",
+       "to 30% or more       3     8"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gender_pivot = finance.pivot_table(index = \"Percentage\", columns = \"Gender\", aggfunc=\"size\", fill_value=0)\n",
+    "gender_pivot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[4, 16], [9, 5], [3, 8]]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "resultado_gender = gender_pivot.values.tolist()\n",
+    "resultado_gender"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Chi2ContingencyResult(statistic=7.4846758844603665, pvalue=0.023698632279544475, dof=2, expected_freq=array([[ 7.11111111, 12.88888889],\n",
+       "       [ 4.97777778,  9.02222222],\n",
+       "       [ 3.91111111,  7.08888889]]))"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## H0: the gender are independent of the investment\n",
+    "## H1: the gender are dependent of the investment\n",
+    "\n",
+    "alpha = 0.05\n",
+    "st.chi2_contingency(resultado_gender)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x17b75cf5360>,\n",
+       " <matplotlib.lines.Line2D at 0x17b75cf5300>]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(gender_pivot)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Education x Risk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "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",
+       "        vertical-align: top;\n",
+       "    }\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>Risk</th>\n",
+       "      <th>Earn more with low risk</th>\n",
+       "      <th>I don't intend to invest</th>\n",
+       "      <th>Not afraid to take risks</th>\n",
+       "      <th>Preserve capital with no risk</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Education</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Bachelor´s Degree</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Master or plus</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Postgraduate Degree</th>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Primary School</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Secondary School</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Risk                 Earn more with low risk  I don't intend to invest  \\\n",
+       "Education                                                                \n",
+       "Bachelor´s Degree                          7                         2   \n",
+       "Master or plus                             4                         1   \n",
+       "Postgraduate Degree                        6                         2   \n",
+       "Primary School                             0                         0   \n",
+       "Secondary School                           5                         2   \n",
+       "\n",
+       "Risk                 Not afraid to take risks  Preserve capital with no risk  \n",
+       "Education                                                                     \n",
+       "Bachelor´s Degree                           2                              4  \n",
+       "Master or plus                              4                              3  \n",
+       "Postgraduate Degree                         1                              3  \n",
+       "Primary School                              0                              1  \n",
+       "Secondary School                            3                              8  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "education_pivot = finance.pivot_table(index = \"Education\", columns = \"Risk\", aggfunc=\"size\",fill_value=0)\n",
+    "education_pivot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[7, 2, 2, 4], [4, 1, 4, 3], [6, 2, 1, 3], [0, 0, 0, 1], [5, 2, 3, 8]]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "resultado_education = education_pivot.values.tolist()\n",
+    "resultado_education"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Chi2ContingencyResult(statistic=7.606008202323991, pvalue=0.8151123019796717, dof=12, expected_freq=array([[5.68965517, 1.81034483, 2.5862069 , 4.9137931 ],\n",
+       "       [4.55172414, 1.44827586, 2.06896552, 3.93103448],\n",
+       "       [4.55172414, 1.44827586, 2.06896552, 3.93103448],\n",
+       "       [0.37931034, 0.12068966, 0.17241379, 0.32758621],\n",
+       "       [6.82758621, 2.17241379, 3.10344828, 5.89655172]]))"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## H0: the education status are independent of the risk\n",
+    "## H1: the education status are dependent of the risk\n",
+    "\n",
+    "\n",
+    "alpha = 0.05\n",
+    "st.chi2_contingency(resultado_education)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x17b75d70c70>,\n",
+       " <matplotlib.lines.Line2D at 0x17b75d70c10>,\n",
+       " <matplotlib.lines.Line2D at 0x17b75d70c40>,\n",
+       " <matplotlib.lines.Line2D at 0x17b75d70dc0>]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(education_pivot)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "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",
+       "        vertical-align: top;\n",
+       "    }\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>Risk</th>\n",
+       "      <th>Risk</th>\n",
+       "      <th>I don't intend to invest</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Education</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Bachelor´s Degree</th>\n",
+       "      <td>13</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Master or plus</th>\n",
+       "      <td>11</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Postgraduate Degree</th>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Primary School</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Secondary School</th>\n",
+       "      <td>16</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Risk                 Risk  I don't intend to invest\n",
+       "Education                                          \n",
+       "Bachelor´s Degree      13                         2\n",
+       "Master or plus         11                         1\n",
+       "Postgraduate Degree    10                         2\n",
+       "Primary School          1                         0\n",
+       "Secondary School       16                         2"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "education_pivot[\"Risk\"] = education_pivot[\"Earn more with low risk\"] + education_pivot[\"Not afraid to take risks\"] + education_pivot[\"Preserve capital with no risk\"]\n",
+    "\n",
+    "new_education_pivot = education_pivot[[\"Risk\", \"I don't intend to invest\"]]\n",
+    "new_education_pivot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[13, 2], [11, 1], [10, 2], [1, 0], [16, 2]]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_resultado_education = new_education_pivot.values.tolist()\n",
+    "new_resultado_education"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Chi2ContingencyResult(statistic=0.5722377840024899, pvalue=0.9660981750498169, dof=4, expected_freq=array([[13.18965517,  1.81034483],\n",
+       "       [10.55172414,  1.44827586],\n",
+       "       [10.55172414,  1.44827586],\n",
+       "       [ 0.87931034,  0.12068966],\n",
+       "       [15.82758621,  2.17241379]]))"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## H0: the education status are independent of the investment\n",
+    "## H1: the education status are dependent of the investment\n",
+    "\n",
+    "alpha = 0.05\n",
+    "st.chi2_contingency(new_resultado_education)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x17b75ddf700>,\n",
+       " <matplotlib.lines.Line2D at 0x17b75ddf760>]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(new_resultado_education)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "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",
+       "        vertical-align: top;\n",
+       "    }\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>Risk</th>\n",
+       "      <th>Education</th>\n",
+       "      <th>Risk</th>\n",
+       "      <th>I don't intend to invest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bachelor´s Degree</td>\n",
+       "      <td>13</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Master or plus</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Postgraduate Degree</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Primary School</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Secondary School</td>\n",
+       "      <td>16</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Risk            Education  Risk  I don't intend to invest\n",
+       "0       Bachelor´s Degree    13                         2\n",
+       "1          Master or plus    11                         1\n",
+       "2     Postgraduate Degree    10                         2\n",
+       "3          Primary School     1                         0\n",
+       "4        Secondary School    16                         2"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "finance_risk = new_education_pivot.reset_index()\n",
+    "finance_risk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "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",
+       "        vertical-align: top;\n",
+       "    }\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>Risk</th>\n",
+       "      <th>Education</th>\n",
+       "      <th>Risk</th>\n",
+       "      <th>I don't intend to invest</th>\n",
+       "      <th>Risk_perc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bachelor´s Degree</td>\n",
+       "      <td>13</td>\n",
+       "      <td>2</td>\n",
+       "      <td>25.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Master or plus</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1</td>\n",
+       "      <td>21.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Postgraduate Degree</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>19.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Primary School</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Secondary School</td>\n",
+       "      <td>16</td>\n",
+       "      <td>2</td>\n",
+       "      <td>31.37</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Risk            Education  Risk  I don't intend to invest  Risk_perc\n",
+       "0       Bachelor´s Degree    13                         2      25.49\n",
+       "1          Master or plus    11                         1      21.57\n",
+       "2     Postgraduate Degree    10                         2      19.61\n",
+       "3          Primary School     1                         0       1.96\n",
+       "4        Secondary School    16                         2      31.37"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "finance_risk[\"Risk_perc\"] = round(finance_risk[\"Risk\"]/finance_risk[\"Risk\"].sum()*100,2)\n",
+    "finance_risk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "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",
+       "        vertical-align: top;\n",
+       "    }\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>Risk</th>\n",
+       "      <th>Education</th>\n",
+       "      <th>Risk</th>\n",
+       "      <th>I don't intend to invest</th>\n",
+       "      <th>Risk_perc</th>\n",
+       "      <th>No Invest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bachelor´s Degree</td>\n",
+       "      <td>13</td>\n",
+       "      <td>2</td>\n",
+       "      <td>25.49</td>\n",
+       "      <td>28.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Master or plus</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1</td>\n",
+       "      <td>21.57</td>\n",
+       "      <td>14.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Postgraduate Degree</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>19.61</td>\n",
+       "      <td>28.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Primary School</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.96</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Secondary School</td>\n",
+       "      <td>16</td>\n",
+       "      <td>2</td>\n",
+       "      <td>31.37</td>\n",
+       "      <td>28.57</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Risk            Education  Risk  I don't intend to invest  Risk_perc  \\\n",
+       "0       Bachelor´s Degree    13                         2      25.49   \n",
+       "1          Master or plus    11                         1      21.57   \n",
+       "2     Postgraduate Degree    10                         2      19.61   \n",
+       "3          Primary School     1                         0       1.96   \n",
+       "4        Secondary School    16                         2      31.37   \n",
+       "\n",
+       "Risk  No Invest  \n",
+       "0         28.57  \n",
+       "1         14.29  \n",
+       "2         28.57  \n",
+       "3          0.00  \n",
+       "4         28.57  "
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "finance_risk[\"No Invest\"] = round(finance_risk[\"I don't intend to invest\"]/finance_risk[\"I don't intend to invest\"].sum()*100,2)\n",
+    "finance_risk"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SAVE FILES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "finance_risk.to_csv(\"finance_risk.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "finance.to_csv(\"finance.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F-statistic: 0.17538622690192374\n",
+      "P-value: 0.6769705345873518\n",
+      "          Multiple Comparison of Means - Tukey HSD, FWER=0.05           \n",
+      "========================================================================\n",
+      "      group1           group2     meandiff p-adj  lower    upper  reject\n",
+      "------------------------------------------------------------------------\n",
+      "Slightly important Very important  -2.7308 0.677 -15.7931 10.3316  False\n",
+      "------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Perform ANOVA test\n",
+    "f_statistic, p_value = f_oneway(finance[finance[\"Importance\"] == \"Very important\"][\"Age\"],\n",
+    "                                 finance[finance[\"Importance\"] == \"Slightly important\"][\"Age\"])\n",
+    "# Print the ANOVA test results\n",
+    "print(\"F-statistic:\", f_statistic)\n",
+    "print(\"P-value:\", p_value)\n",
+    "# Perform Tukey's HSD test\n",
+    "tukey_result = pairwise_tukeyhsd(finance[\"Age\"],finance[\"Importance\"])\n",
+    "# Print Tukey's HSD results\n",
+    "print(tukey_result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Importance\n",
+       "Very important        52\n",
+       "Slightly important     6\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "finance[\"Importance\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "finance[\"Age\"] = finance[\"Age\"].astype(float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([24, 32, 23, 39, 35, 16, 43, 17, 30, 44, 22, 29, 42, 45, 34, 27, 25,\n",
+       "       31, 52, 75, 47, 73, 63, 78, 46, 40, 41, 50, 55, 33, 36, 28, 37])"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "finance[\"Age\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "finance[\"Age\"].isnull().sum()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.10.9"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/your-project/Financial Research Questionnaire.csv b/your-project/Financial Research Questionnaire.csv
new file mode 100644
index 0000000..7554fcf
--- /dev/null
+++ b/your-project/Financial Research Questionnaire.csv	
@@ -0,0 +1,61 @@
+"Carimbo de data/hora","01 / 13 - Gender","02 / 13 - Whats your age? (only number)","03 / 13 - Marital Status:","04 / 13 - Education Level","05 / 13 - Monthly Income (x min wage)","06 / 13 - Importance of Financial Education","07 /13 - Source of Financial Education","08 / 13 - Level of Knowledge in the Stock Market","09 / 13 - Financial Planning and Control","10 / 13 - Percentage of Salary Invested","11 / 13 - Types of Investments","12 / 13 - Investment Objectives","13 / 13 - What´s your acceptance of risk in invested capital"
+"2023/08/13 2:53:39 PM GMT+1","Female","39 to 55","Married or Living together","Postgraduate Degree","Up to 1 minimum wage","Very important","Family and friends","Medium","Medium (I define my expenses in a basic way)","to 10%","Savings account","Retirement","Preserve capital with no risk"
+"2023/08/13 2:54:47 PM GMT+1","Male","39 to 55","Married or Living together","Postgraduate Degree","Above 5 minimum wages","Very important","Self-taught","High","Medium (I define my expenses in a basic way)","to 30%","Stocks","Retirement","Earn more with low risk"
+"2023/08/14 2:35:01 PM GMT+1","Male","24","Married or Living together","Secondary School","1","Very important","Self-taught","1","Medium (I define my expenses in a basic way)","None","None","Retirement;Emergencies","Earn more with low risk"
+"2023/08/14 2:35:35 PM GMT+1","Male","32","Single or Living alone","Bachelor´s Degree","3","Slightly important","Self-taught","1","Medium (I define my expenses in a basic way)","to 10%","Real estate;crypto","Retirement;Children future","Not afraid to take risks"
+"2023/08/14 2:36:08 PM GMT+1","Female","23","Single or Living alone","Bachelor´s Degree","1","Very important","Family and friends","0","Medium (I define my expenses in a basic way)","to 10%","Savings account","Other","Earn more with low risk"
+"2023/08/14 2:37:16 PM GMT+1","Male","39","Married or Living together","Master or plus","1","Very important","Family and friends","2","High (I categorize all my expenses)","to 10%","Stocks;Savings account","Retirement;Children future;Emergencies;Unemployment insurance","Preserve capital with no risk"
+"2023/08/14 2:38:05 PM GMT+1","Male","23","Single or Living alone","Secondary School","2","Very important","Family and friends","2","Medium (I define my expenses in a basic way)","to 10%","Savings account","Retirement;Children future;Emergencies;Unemployment insurance","Earn more with low risk"
+"2023/08/14 2:38:12 PM GMT+1","Female","35","Married or Living together","Postgraduate Degree","2","Very important","Family and friends","0","Medium (I define my expenses in a basic way)","to 10%","Savings account","Emergencies","Preserve capital with no risk"
+"2023/08/14 2:41:58 PM GMT+1","Female","16","Single or Living alone","Secondary School","1","Very important","No knowledge","2","Medium (I define my expenses in a basic way)","to 30% or more","Savings account","Emergencies","Earn more with low risk"
+"2023/08/14 2:42:24 PM GMT+1","Female","43","Married or Living together","Postgraduate Degree","2","Very important","Family and friends","1","Medium (I define my expenses in a basic way)","to 20%","Real estate;Savings account","Retirement;Children future;Unemployment insurance","Earn more with low risk"
+"2023/08/14 2:43:47 PM GMT+1","Female","17","Single or Living alone","Secondary School","5","Very important","School","2","Medium (I define my expenses in a basic way)","to 20%","Stocks;Real estate;Savings account","Children future;Emergencies;Unemployment insurance","Preserve capital with no risk"
+"2023/08/14 2:43:58 PM GMT+1","Male","30","Married or Living together","Master or plus","1","Very important","School","2","Medium (I define my expenses in a basic way)","to 20%","Real estate;Savings account;Crypto","Retirement;Children future","Earn more with low risk"
+"2023/08/14 2:44:08 PM GMT+1","Female","16 anos","Single or Living alone","Secondary School","5","Very important","School","2","Medium (I define my expenses in a basic way)","to 20%","Stocks;Real estate;Savings account","Children future;Emergencies;Unemployment insurance","Preserve capital with no risk"
+"2023/08/14 2:44:57 PM GMT+1","Female","16","Single or Living alone","Secondary School","5","Slightly important","Family and friends","0","Medium (I define my expenses in a basic way)","None","None","Other","I don't intend to invest"
+"2023/08/14 2:45:27 PM GMT+1","Female","44","Single or Living alone","Postgraduate Degree","5","Very important","No knowledge","0","Low (I don't know where my money goes)","None","None","Other","I don't intend to invest"
+"2023/08/14 2:47:35 PM GMT+1","Male","16","Single or Living alone","Secondary School","1","Very important","Family and friends","1","Medium (I define my expenses in a basic way)","to 10%","Real estate","Other","Preserve capital with no risk"
+"2023/08/14 2:48:18 PM GMT+1","Male","22","Single or Living alone","Postgraduate Degree","2","Very important","Self-taught","0","Medium (I define my expenses in a basic way)","None","None","Other","I don't intend to invest"
+"2023/08/14 2:50:30 PM GMT+1","Male","29","Married or Living together","Bachelor´s Degree","4","Very important","Family and friends","0","Medium (I define my expenses in a basic way)","to 10%","Savings account","Unemployment insurance","Preserve capital with no risk"
+"2023/08/14 2:58:57 PM GMT+1","Male","17","Single or Living alone","Secondary School","4","Very important","School","2","Medium (I define my expenses in a basic way)","None","Savings account","Children future","I don't intend to invest"
+"2023/08/14 3:01:50 PM GMT+1","Female","42","Married or Living together","Bachelor´s Degree","5","Very important","No knowledge","0","Medium (I define my expenses in a basic way)","None","private pension","Retirement","Earn more with low risk"
+"2023/08/14 3:15:02 PM GMT+1","Male","45","Single or Living alone","Master or plus","3","Very important","Family and friends","0","Medium (I define my expenses in a basic way)","to 10%","Savings account","Retirement;Emergencies;Unemployment insurance","Earn more with low risk"
+"2023/08/14 3:59:20 PM GMT+1","Male","34","Married or Living together","Secondary School","1","Very important","Self-taught","2","High (I categorize all my expenses)","to 10%","Savings account","Unemployment insurance","Preserve capital with no risk"
+"2023/08/14 4:07:42 PM GMT+1","Female","27","Married or Living together","Secondary School","1","Very important","Self-taught","0","Medium (I define my expenses in a basic way)","None","Real estate;Savings account","Children future;Emergencies","Not afraid to take risks"
+"2023/08/14 5:13:46 PM GMT+1","Male","25","Married or Living together","Secondary School","3","Very important","Self-taught","4","Medium (I define my expenses in a basic way)","to 30% or more","Stocks","Other","Not afraid to take risks"
+"2023/08/14 5:16:20 PM GMT+1","Male","31","Single or Living alone","Bachelor´s Degree","2","Very important","Self-taught","1","Medium (I define my expenses in a basic way)","to 20%","Real estate","Retirement;Children future;Emergencies","Earn more with low risk"
+"2023/08/14 5:18:16 PM GMT+1","Male","52","Single or Living alone","Bachelor´s Degree","5","Very important","Self-taught","2","Medium (I define my expenses in a basic way)","to 20%","Savings account","Retirement;Emergencies","Earn more with low risk"
+"2023/08/14 6:44:47 PM GMT+1","Male","75","Married or Living together","Postgraduate Degree","5","Very important","No knowledge","2","Low (I don't know where my money goes)","None","Savings account","Emergencies","Preserve capital with no risk"
+"2023/08/14 7:16:08 PM GMT+1","Male","35","Married or Living together","Master or plus","3","Very important","School","0","High (I categorize all my expenses)","to 10%","Savings account","Children future","Earn more with low risk"
+"2023/08/14 7:18:26 PM GMT+1","Male","31","Single or Living alone","Secondary School","2","Very important","No knowledge","0","Low (I don't know where my money goes)","None","None","Other","Earn more with low risk"
+"2023/08/14 7:24:29 PM GMT+1","Female","35","Married or Living together","Bachelor´s Degree","1","Very important","Self-taught","0","Medium (I define my expenses in a basic way)","None","None","Children future;Emergencies","I don't intend to invest"
+"2023/08/14 7:26:54 PM GMT+1","Male","47","Married or Living together","Bachelor´s Degree","5","Very important","Self-taught","2","Medium (I define my expenses in a basic way)","to 10%","Stocks;Real estate;Savings account","Retirement;Children future;Emergencies","Earn more with low risk"
+"2023/08/14 7:33:05 PM GMT+1","Female","22","Married or Living together","Bachelor´s Degree","1","Very important","School","0","Medium (I define my expenses in a basic way)","to 10%","Savings account","Other","Preserve capital with no risk"
+"2023/08/14 8:43:51 PM GMT+1","Male","75","Married or Living together","Secondary School","2","Very important","Self-taught","1","Medium (I define my expenses in a basic way)","to 10%","Savings account","Retirement","Preserve capital with no risk"
+"2023/08/14 8:44:43 PM GMT+1","Male","73","Married or Living together","Secondary School","2","Very important","No knowledge","0","High (I categorize all my expenses)","to 10%","Savings account","Retirement","Preserve capital with no risk"
+"2023/08/14 8:45:29 PM GMT+1","Female","63","Married or Living together","Primary School","2","Very important","No knowledge","0","Medium (I define my expenses in a basic way)","to 10%","Savings account","Retirement","Preserve capital with no risk"
+"2023/08/14 8:46:08 PM GMT+1","Female","78","Single or Living alone","Secondary School","1","Very important","No knowledge","0","Medium (I define my expenses in a basic way)","to 20%","Savings account","Retirement","Preserve capital with no risk"
+"2023/08/14 8:47:35 PM GMT+1","Female","46","Married or Living together","Master or plus","5","Very important","Self-taught","1","High (I categorize all my expenses)","to 30% or more","Real estate;Savings account","Retirement;Children future;Emergencies","Earn more with low risk"
+"2023/08/14 8:48:17 PM GMT+1","Male","46","Married or Living together","Postgraduate Degree","5","Slightly important","Family and friends","1","High (I categorize all my expenses)","to 30% or more","Real estate;Savings account","Retirement;Children future;Emergencies","Earn more with low risk"
+"2023/08/14 8:48:55 PM GMT+1","Female","45","Single or Living alone","Bachelor´s Degree","1","Very important","Family and friends","1","High (I categorize all my expenses)","to 20%","Savings account","Retirement;Emergencies","Preserve capital with no risk"
+"2023/08/14 8:50:09 PM GMT+1","Female","40","Married or Living together","Postgraduate Degree","4","Very important","Family and friends","1","High (I categorize all my expenses)","to 20%","Savings account","Retirement;Children future","Earn more with low risk"
+"2023/08/14 8:50:40 PM GMT+1","Male","43","Married or Living together","Postgraduate Degree","5","Very important","Self-taught","2","High (I categorize all my expenses)","to 30% or more","Stocks;Savings account","Retirement;Children future","Earn more with low risk"
+"2023/08/14 9:20:06 PM GMT+1","Male","23","Single or Living alone","Bachelor´s Degree","2","Very important","Family and friends","0","Medium (I define my expenses in a basic way)","None","Savings account","Other","I don't intend to invest"
+"2023/08/14 9:22:02 PM GMT+1","Female","46","Married or Living together","Bachelor´s Degree","4","Very important","Family and friends","1","Medium (I define my expenses in a basic way)","to 20%","Savings account","Retirement;Children future;Unemployment insurance","Earn more with low risk"
+"2023/08/14 9:22:29 PM GMT+1","Female","44","Married or Living together","Postgraduate Degree","1","Very important","Family and friends","1","Medium (I define my expenses in a basic way)","to 30% or more","Savings account","Retirement;Children future","Preserve capital with no risk"
+"2023/08/14 9:23:00 PM GMT+1","Female","43","Single or Living alone","Postgraduate Degree","5","Very important","Family and friends","1","Medium (I define my expenses in a basic way)","to 20%","Savings account","Retirement;Children future;Emergencies;Unemployment insurance","Earn more with low risk"
+"2023/08/14 9:23:42 PM GMT+1","Male","41","Single or Living alone","Master or plus","1","Slightly important","Family and friends","1","Medium (I define my expenses in a basic way)","to 10%","Savings account","Retirement;Emergencies","Preserve capital with no risk"
+"2023/08/14 9:24:38 PM GMT+1","Male","44","Married or Living together","Postgraduate Degree","5","Very important","Self-taught","2","High (I categorize all my expenses)","to 30% or more","Stocks;Real estate;Savings account","Retirement","Earn more with low risk"
+"2023/08/14 9:25:31 PM GMT+1","Male","50","Married or Living together","Bachelor´s Degree","4","Slightly important","No knowledge","0","Medium (I define my expenses in a basic way)","to 10%","Savings account","Emergencies","Preserve capital with no risk"
+"2023/08/14 9:26:04 PM GMT+1","Male","55","Married or Living together","Secondary School","5","Slightly important","Family and friends","1","Medium (I define my expenses in a basic way)","to 20%","Real estate;Savings account","Retirement;Emergencies","Earn more with low risk"
+"2023/08/14 9:37:33 PM GMT+1","Male","42","Married or Living together","Bachelor´s Degree","5","Very important","Self-taught","2","High (I categorize all my expenses)","to 30% or more","Stocks;Real estate;Savings account","Retirement;Children future;Emergencies;Unemployment insurance","Not afraid to take risks"
+"2023/08/14 10:05:09 PM GMT+1","Male","33","Married or Living together","Secondary School","3","Very important","Self-taught","0","Medium (I define my expenses in a basic way)","to 10%","Savings account","Other","Preserve capital with no risk"
+"2023/08/15 2:37:42 AM GMT+1","Female","40","Married or Living together","Master or plus","2","Very important","No knowledge","1","Medium (I define my expenses in a basic way)","to 20%","None","Retirement","I don't intend to invest"
+"2023/08/15 10:22:24 AM GMT+1","Male","45","Married or Living together","Master or plus","3","Very important","School","4","Medium (I define my expenses in a basic way)","None","Real estate;Savings account","Retirement;Children future","Preserve capital with no risk"
+"2023/08/15 10:41:50 AM GMT+1","Male","25","Single or Living alone","Master or plus","3","Very important","Self-taught","3","Medium (I define my expenses in a basic way)","to 30% or more","Stocks","Retirement","Not afraid to take risks"
+"2023/08/15 11:42:12 AM GMT+1","Male","41","Married or Living together","Postgraduate Degree","5","Very important","Self-taught","2","Medium (I define my expenses in a basic way)","to 20%","Stocks;Real estate","Retirement;Children future;Emergencies;Unemployment insurance","Not afraid to take risks"
+"2023/08/15 2:03:39 PM GMT+1","Male","36","Married or Living together","Master or plus","3","Very important","Self-taught","2","High (I categorize all my expenses)","to 10%","Stocks;Real estate;Savings account","Retirement;Emergencies;Unemployment insurance;Other","Not afraid to take risks"
+"2023/08/15 2:51:37 PM GMT+1","Male","28","Single or Living alone","Master or plus","4","Very important","Self-taught","3","High (I categorize all my expenses)","to 30% or more","Stocks;Savings account","Retirement;Other","Not afraid to take risks"
+"2023/08/15 5:14:20 PM GMT+1","Male","37","Married or Living together","Secondary School","5","Very important","Self-taught","2","Medium (I define my expenses in a basic way)","to 10%","Stocks;Savings account","Retirement;Emergencies","Not afraid to take risks"
+"2023/08/15 7:14:18 PM GMT+1","Female","25","Married or Living together","Bachelor´s Degree","2","Very important","Self-taught","0","Medium (I define my expenses in a basic way)","None","None","Retirement;Children future;Emergencies","Earn more with low risk"
+"2023/08/15 8:04:12 PM GMT+1","Male","28","Single or Living alone","Master or plus","5","Very important","Self-taught","3","High (I categorize all my expenses)","to 30% or more","Stocks","Retirement;Children future;Emergencies;Unemployment insurance;Other","Not afraid to take risks"
\ No newline at end of file
diff --git a/your-project/GPU.pdf b/your-project/GPU.pdf
new file mode 100644
index 0000000..a0f2322
Binary files /dev/null and b/your-project/GPU.pdf differ
diff --git a/your-project/Tableau Link.txt b/your-project/Tableau Link.txt
new file mode 100644
index 0000000..cdece1f
--- /dev/null
+++ b/your-project/Tableau Link.txt	
@@ -0,0 +1 @@
+https://public.tableau.com/app/profile/s.rgio.fl.rido/viz/Project6-Analytics/FINANCIALRESEARCH?publish=yes
\ No newline at end of file
diff --git a/your-project/finance.csv b/your-project/finance.csv
new file mode 100644
index 0000000..3fc89d4
--- /dev/null
+++ b/your-project/finance.csv
@@ -0,0 +1,59 @@
+Gender,Age,Marital,Education,Salary,Importance,Source,Knowledge,Control,Percentage,Investments,Objectives,Risk
+Male,24,Married or Living together,Secondary School,1,Very important,Self-taught,1,Medium (I define my expenses in a basic way),,,Retirement;Emergencies,Earn more with low risk
+Male,32,Single or Living alone,Bachelor´s Degree,3,Slightly important,Self-taught,1,Medium (I define my expenses in a basic way),to 10%,Real estate;crypto,Retirement;Children future,Not afraid to take risks
+Female,23,Single or Living alone,Bachelor´s Degree,1,Very important,Family and friends,0,Medium (I define my expenses in a basic way),to 10%,Savings account,Other,Earn more with low risk
+Male,39,Married or Living together,Master or plus,1,Very important,Family and friends,2,High (I categorize all my expenses),to 10%,Stocks;Savings account,Retirement;Children future;Emergencies;Unemployment insurance,Preserve capital with no risk
+Male,23,Single or Living alone,Secondary School,2,Very important,Family and friends,2,Medium (I define my expenses in a basic way),to 10%,Savings account,Retirement;Children future;Emergencies;Unemployment insurance,Earn more with low risk
+Female,35,Married or Living together,Postgraduate Degree,2,Very important,Family and friends,0,Medium (I define my expenses in a basic way),to 10%,Savings account,Emergencies,Preserve capital with no risk
+Female,16,Single or Living alone,Secondary School,1,Very important,No knowledge,2,Medium (I define my expenses in a basic way),to 30% or more,Savings account,Emergencies,Earn more with low risk
+Female,43,Married or Living together,Postgraduate Degree,2,Very important,Family and friends,1,Medium (I define my expenses in a basic way),to 20%,Real estate;Savings account,Retirement;Children future;Unemployment insurance,Earn more with low risk
+Female,17,Single or Living alone,Secondary School,5,Very important,School,2,Medium (I define my expenses in a basic way),to 20%,Stocks;Real estate;Savings account,Children future;Emergencies;Unemployment insurance,Preserve capital with no risk
+Male,30,Married or Living together,Master or plus,1,Very important,School,2,Medium (I define my expenses in a basic way),to 20%,Real estate;Savings account;Crypto,Retirement;Children future,Earn more with low risk
+Female,16,Single or Living alone,Secondary School,5,Very important,School,2,Medium (I define my expenses in a basic way),to 20%,Stocks;Real estate;Savings account,Children future;Emergencies;Unemployment insurance,Preserve capital with no risk
+Female,16,Single or Living alone,Secondary School,5,Slightly important,Family and friends,0,Medium (I define my expenses in a basic way),,,Other,I don't intend to invest
+Female,44,Single or Living alone,Postgraduate Degree,5,Very important,No knowledge,0,Low (I don't know where my money goes),,,Other,I don't intend to invest
+Male,16,Single or Living alone,Secondary School,1,Very important,Family and friends,1,Medium (I define my expenses in a basic way),to 10%,Real estate,Other,Preserve capital with no risk
+Male,22,Single or Living alone,Postgraduate Degree,2,Very important,Self-taught,0,Medium (I define my expenses in a basic way),,,Other,I don't intend to invest
+Male,29,Married or Living together,Bachelor´s Degree,4,Very important,Family and friends,0,Medium (I define my expenses in a basic way),to 10%,Savings account,Unemployment insurance,Preserve capital with no risk
+Male,17,Single or Living alone,Secondary School,4,Very important,School,2,Medium (I define my expenses in a basic way),,Savings account,Children future,I don't intend to invest
+Female,42,Married or Living together,Bachelor´s Degree,5,Very important,No knowledge,0,Medium (I define my expenses in a basic way),,private pension,Retirement,Earn more with low risk
+Male,45,Single or Living alone,Master or plus,3,Very important,Family and friends,0,Medium (I define my expenses in a basic way),to 10%,Savings account,Retirement;Emergencies;Unemployment insurance,Earn more with low risk
+Male,34,Married or Living together,Secondary School,1,Very important,Self-taught,2,High (I categorize all my expenses),to 10%,Savings account,Unemployment insurance,Preserve capital with no risk
+Female,27,Married or Living together,Secondary School,1,Very important,Self-taught,0,Medium (I define my expenses in a basic way),,Real estate;Savings account,Children future;Emergencies,Not afraid to take risks
+Male,25,Married or Living together,Secondary School,3,Very important,Self-taught,4,Medium (I define my expenses in a basic way),to 30% or more,Stocks,Other,Not afraid to take risks
+Male,31,Single or Living alone,Bachelor´s Degree,2,Very important,Self-taught,1,Medium (I define my expenses in a basic way),to 20%,Real estate,Retirement;Children future;Emergencies,Earn more with low risk
+Male,52,Single or Living alone,Bachelor´s Degree,5,Very important,Self-taught,2,Medium (I define my expenses in a basic way),to 20%,Savings account,Retirement;Emergencies,Earn more with low risk
+Male,75,Married or Living together,Postgraduate Degree,5,Very important,No knowledge,2,Low (I don't know where my money goes),,Savings account,Emergencies,Preserve capital with no risk
+Male,35,Married or Living together,Master or plus,3,Very important,School,0,High (I categorize all my expenses),to 10%,Savings account,Children future,Earn more with low risk
+Male,31,Single or Living alone,Secondary School,2,Very important,No knowledge,0,Low (I don't know where my money goes),,,Other,Earn more with low risk
+Female,35,Married or Living together,Bachelor´s Degree,1,Very important,Self-taught,0,Medium (I define my expenses in a basic way),,,Children future;Emergencies,I don't intend to invest
+Male,47,Married or Living together,Bachelor´s Degree,5,Very important,Self-taught,2,Medium (I define my expenses in a basic way),to 10%,Stocks;Real estate;Savings account,Retirement;Children future;Emergencies,Earn more with low risk
+Female,22,Married or Living together,Bachelor´s Degree,1,Very important,School,0,Medium (I define my expenses in a basic way),to 10%,Savings account,Other,Preserve capital with no risk
+Male,75,Married or Living together,Secondary School,2,Very important,Self-taught,1,Medium (I define my expenses in a basic way),to 10%,Savings account,Retirement,Preserve capital with no risk
+Male,73,Married or Living together,Secondary School,2,Very important,No knowledge,0,High (I categorize all my expenses),to 10%,Savings account,Retirement,Preserve capital with no risk
+Female,63,Married or Living together,Primary School,2,Very important,No knowledge,0,Medium (I define my expenses in a basic way),to 10%,Savings account,Retirement,Preserve capital with no risk
+Female,78,Single or Living alone,Secondary School,1,Very important,No knowledge,0,Medium (I define my expenses in a basic way),to 20%,Savings account,Retirement,Preserve capital with no risk
+Female,46,Married or Living together,Master or plus,5,Very important,Self-taught,1,High (I categorize all my expenses),to 30% or more,Real estate;Savings account,Retirement;Children future;Emergencies,Earn more with low risk
+Male,46,Married or Living together,Postgraduate Degree,5,Slightly important,Family and friends,1,High (I categorize all my expenses),to 30% or more,Real estate;Savings account,Retirement;Children future;Emergencies,Earn more with low risk
+Female,45,Single or Living alone,Bachelor´s Degree,1,Very important,Family and friends,1,High (I categorize all my expenses),to 20%,Savings account,Retirement;Emergencies,Preserve capital with no risk
+Female,40,Married or Living together,Postgraduate Degree,4,Very important,Family and friends,1,High (I categorize all my expenses),to 20%,Savings account,Retirement;Children future,Earn more with low risk
+Male,43,Married or Living together,Postgraduate Degree,5,Very important,Self-taught,2,High (I categorize all my expenses),to 30% or more,Stocks;Savings account,Retirement;Children future,Earn more with low risk
+Male,23,Single or Living alone,Bachelor´s Degree,2,Very important,Family and friends,0,Medium (I define my expenses in a basic way),,Savings account,Other,I don't intend to invest
+Female,46,Married or Living together,Bachelor´s Degree,4,Very important,Family and friends,1,Medium (I define my expenses in a basic way),to 20%,Savings account,Retirement;Children future;Unemployment insurance,Earn more with low risk
+Female,44,Married or Living together,Postgraduate Degree,1,Very important,Family and friends,1,Medium (I define my expenses in a basic way),to 30% or more,Savings account,Retirement;Children future,Preserve capital with no risk
+Female,43,Single or Living alone,Postgraduate Degree,5,Very important,Family and friends,1,Medium (I define my expenses in a basic way),to 20%,Savings account,Retirement;Children future;Emergencies;Unemployment insurance,Earn more with low risk
+Male,41,Single or Living alone,Master or plus,1,Slightly important,Family and friends,1,Medium (I define my expenses in a basic way),to 10%,Savings account,Retirement;Emergencies,Preserve capital with no risk
+Male,44,Married or Living together,Postgraduate Degree,5,Very important,Self-taught,2,High (I categorize all my expenses),to 30% or more,Stocks;Real estate;Savings account,Retirement,Earn more with low risk
+Male,50,Married or Living together,Bachelor´s Degree,4,Slightly important,No knowledge,0,Medium (I define my expenses in a basic way),to 10%,Savings account,Emergencies,Preserve capital with no risk
+Male,55,Married or Living together,Secondary School,5,Slightly important,Family and friends,1,Medium (I define my expenses in a basic way),to 20%,Real estate;Savings account,Retirement;Emergencies,Earn more with low risk
+Male,42,Married or Living together,Bachelor´s Degree,5,Very important,Self-taught,2,High (I categorize all my expenses),to 30% or more,Stocks;Real estate;Savings account,Retirement;Children future;Emergencies;Unemployment insurance,Not afraid to take risks
+Male,33,Married or Living together,Secondary School,3,Very important,Self-taught,0,Medium (I define my expenses in a basic way),to 10%,Savings account,Other,Preserve capital with no risk
+Female,40,Married or Living together,Master or plus,2,Very important,No knowledge,1,Medium (I define my expenses in a basic way),to 20%,,Retirement,I don't intend to invest
+Male,45,Married or Living together,Master or plus,3,Very important,School,4,Medium (I define my expenses in a basic way),,Real estate;Savings account,Retirement;Children future,Preserve capital with no risk
+Male,25,Single or Living alone,Master or plus,3,Very important,Self-taught,3,Medium (I define my expenses in a basic way),to 30% or more,Stocks,Retirement,Not afraid to take risks
+Male,41,Married or Living together,Postgraduate Degree,5,Very important,Self-taught,2,Medium (I define my expenses in a basic way),to 20%,Stocks;Real estate,Retirement;Children future;Emergencies;Unemployment insurance,Not afraid to take risks
+Male,36,Married or Living together,Master or plus,3,Very important,Self-taught,2,High (I categorize all my expenses),to 10%,Stocks;Real estate;Savings account,Retirement;Emergencies;Unemployment insurance;Other,Not afraid to take risks
+Male,28,Single or Living alone,Master or plus,4,Very important,Self-taught,3,High (I categorize all my expenses),to 30% or more,Stocks;Savings account,Retirement;Other,Not afraid to take risks
+Male,37,Married or Living together,Secondary School,5,Very important,Self-taught,2,Medium (I define my expenses in a basic way),to 10%,Stocks;Savings account,Retirement;Emergencies,Not afraid to take risks
+Female,25,Married or Living together,Bachelor´s Degree,2,Very important,Self-taught,0,Medium (I define my expenses in a basic way),,,Retirement;Children future;Emergencies,Earn more with low risk
+Male,28,Single or Living alone,Master or plus,5,Very important,Self-taught,3,High (I categorize all my expenses),to 30% or more,Stocks,Retirement;Children future;Emergencies;Unemployment insurance;Other,Not afraid to take risks
diff --git a/your-project/finance_risk.csv b/your-project/finance_risk.csv
new file mode 100644
index 0000000..3ebfac9
--- /dev/null
+++ b/your-project/finance_risk.csv
@@ -0,0 +1,6 @@
+Education,Risk,I don't intend to invest,Risk_perc,No Invest
+Bachelor´s Degree,13,2,25.49,28.57
+Master or plus,11,1,21.57,14.29
+Postgraduate Degree,10,2,19.61,28.57
+Primary School,1,0,1.96,0.0
+Secondary School,16,2,31.37,28.57