From f21bdd3a2924267108431ffa996252a240b7ab33 Mon Sep 17 00:00:00 2001
From: "Ladiane P.S" <aladydyljc@gmail.com>
Date: Thu, 29 Aug 2024 22:22:32 -0300
Subject: [PATCH 1/5] Atividade projeto guiado 2

---
 .../para-casa/Ladiane/Apresent_Lady.ipynb     | 1468 +++++++++++++++++
 .../para-casa/Ladiane/MentalHealthSurvey.csv  |   88 +
 .../Ladiane/MentalHealthSurvey_tratado.csv    |   88 +
 exercicios/para-casa/Ladiane/README.md        |  224 +++
 exercicios/para-casa/Ladiane/testeHip.png     |  Bin 0 -> 25842 bytes
 5 files changed, 1868 insertions(+)
 create mode 100644 exercicios/para-casa/Ladiane/Apresent_Lady.ipynb
 create mode 100644 exercicios/para-casa/Ladiane/MentalHealthSurvey.csv
 create mode 100644 exercicios/para-casa/Ladiane/MentalHealthSurvey_tratado.csv
 create mode 100644 exercicios/para-casa/Ladiane/README.md
 create mode 100644 exercicios/para-casa/Ladiane/testeHip.png

diff --git a/exercicios/para-casa/Ladiane/Apresent_Lady.ipynb b/exercicios/para-casa/Ladiane/Apresent_Lady.ipynb
new file mode 100644
index 0000000..c378e77
--- /dev/null
+++ b/exercicios/para-casa/Ladiane/Apresent_Lady.ipynb
@@ -0,0 +1,1468 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df =pd.read_csv(\"MentalHealthSurvey.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "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>university</th>\n",
+       "      <th>degree_level</th>\n",
+       "      <th>degree_major</th>\n",
+       "      <th>academic_year</th>\n",
+       "      <th>cgpa</th>\n",
+       "      <th>residential_status</th>\n",
+       "      <th>campus_discrimination</th>\n",
+       "      <th>sports_engagement</th>\n",
+       "      <th>...</th>\n",
+       "      <th>study_satisfaction</th>\n",
+       "      <th>academic_workload</th>\n",
+       "      <th>academic_pressure</th>\n",
+       "      <th>financial_concerns</th>\n",
+       "      <th>social_relationships</th>\n",
+       "      <th>depression</th>\n",
+       "      <th>anxiety</th>\n",
+       "      <th>isolation</th>\n",
+       "      <th>future_insecurity</th>\n",
+       "      <th>stress_relief_activities</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>20</td>\n",
+       "      <td>PU</td>\n",
+       "      <td>Undergraduate</td>\n",
+       "      <td>Data Science</td>\n",
+       "      <td>2nd year</td>\n",
+       "      <td>3.0-3.5</td>\n",
+       "      <td>Off-Campus</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No Sports</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Religious Activities, Social Connections, Onli...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>20</td>\n",
+       "      <td>UET</td>\n",
+       "      <td>Postgraduate</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>3rd year</td>\n",
+       "      <td>3.0-3.5</td>\n",
+       "      <td>Off-Campus</td>\n",
+       "      <td>No</td>\n",
+       "      <td>1-3 times</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Online Entertainment</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>20</td>\n",
+       "      <td>FAST</td>\n",
+       "      <td>Undergraduate</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>3rd year</td>\n",
+       "      <td>2.5-3.0</td>\n",
+       "      <td>Off-Campus</td>\n",
+       "      <td>No</td>\n",
+       "      <td>1-3 times</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Religious Activities, Sports and Fitness, Onli...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>20</td>\n",
+       "      <td>UET</td>\n",
+       "      <td>Undergraduate</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>3rd year</td>\n",
+       "      <td>2.5-3.0</td>\n",
+       "      <td>On-Campus</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No Sports</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Online Entertainment</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>20</td>\n",
+       "      <td>UET</td>\n",
+       "      <td>Undergraduate</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>3rd year</td>\n",
+       "      <td>3.0-3.5</td>\n",
+       "      <td>Off-Campus</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No Sports</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Online Entertainment</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>20</td>\n",
+       "      <td>UET</td>\n",
+       "      <td>Undergraduate</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>3rd year</td>\n",
+       "      <td>3.0-3.5</td>\n",
+       "      <td>Off-Campus</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No Sports</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Religious Activities, Social Connections, Onli...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>26</td>\n",
+       "      <td>PU</td>\n",
+       "      <td>Postgraduate</td>\n",
+       "      <td>Data Science</td>\n",
+       "      <td>1st year</td>\n",
+       "      <td>2.5-3.0</td>\n",
+       "      <td>On-Campus</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>1-3 times</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Social Connections, Online Entertainment</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>22</td>\n",
+       "      <td>PU</td>\n",
+       "      <td>Undergraduate</td>\n",
+       "      <td>Data Science</td>\n",
+       "      <td>2nd year</td>\n",
+       "      <td>3.0-3.5</td>\n",
+       "      <td>Off-Campus</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No Sports</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Religious Activities, Social Connections, Onli...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>20</td>\n",
+       "      <td>COMSATS</td>\n",
+       "      <td>Undergraduate</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>3rd year</td>\n",
+       "      <td>2.5-3.0</td>\n",
+       "      <td>Off-Campus</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>1-3 times</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Religious Activities, Social Connections, Onli...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>23</td>\n",
+       "      <td>COMSATS</td>\n",
+       "      <td>Undergraduate</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>3rd year</td>\n",
+       "      <td>2.5-3.0</td>\n",
+       "      <td>Off-Campus</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No Sports</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Sports and Fitness</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   gender  age university   degree_level      degree_major academic_year  \\\n",
+       "0    Male   20         PU  Undergraduate      Data Science      2nd year   \n",
+       "1    Male   20        UET   Postgraduate  Computer Science      3rd year   \n",
+       "2    Male   20       FAST  Undergraduate  Computer Science      3rd year   \n",
+       "3    Male   20        UET  Undergraduate  Computer Science      3rd year   \n",
+       "4  Female   20        UET  Undergraduate  Computer Science      3rd year   \n",
+       "5  Female   20        UET  Undergraduate  Computer Science      3rd year   \n",
+       "6    Male   26         PU   Postgraduate      Data Science      1st year   \n",
+       "7    Male   22         PU  Undergraduate      Data Science      2nd year   \n",
+       "8    Male   20    COMSATS  Undergraduate  Computer Science      3rd year   \n",
+       "9    Male   23    COMSATS  Undergraduate  Computer Science      3rd year   \n",
+       "\n",
+       "      cgpa residential_status campus_discrimination sports_engagement  ...  \\\n",
+       "0  3.0-3.5         Off-Campus                    No         No Sports  ...   \n",
+       "1  3.0-3.5         Off-Campus                    No         1-3 times  ...   \n",
+       "2  2.5-3.0         Off-Campus                    No         1-3 times  ...   \n",
+       "3  2.5-3.0          On-Campus                    No         No Sports  ...   \n",
+       "4  3.0-3.5         Off-Campus                   Yes         No Sports  ...   \n",
+       "5  3.0-3.5         Off-Campus                    No         No Sports  ...   \n",
+       "6  2.5-3.0          On-Campus                   Yes         1-3 times  ...   \n",
+       "7  3.0-3.5         Off-Campus                   Yes         No Sports  ...   \n",
+       "8  2.5-3.0         Off-Campus                   Yes         1-3 times  ...   \n",
+       "9  2.5-3.0         Off-Campus                    No         No Sports  ...   \n",
+       "\n",
+       "  study_satisfaction  academic_workload   academic_pressure  \\\n",
+       "0                  5                   4                  5   \n",
+       "1                  5                   4                  4   \n",
+       "2                  5                   5                  5   \n",
+       "3                  3                   5                  4   \n",
+       "4                  3                   5                  5   \n",
+       "5                  4                   5                  5   \n",
+       "6                  4                   4                  4   \n",
+       "7                  3                   4                  4   \n",
+       "8                  3                   4                  3   \n",
+       "9                  3                   5                  3   \n",
+       "\n",
+       "   financial_concerns  social_relationships  depression  anxiety  isolation  \\\n",
+       "0                   4                     3           2        1          1   \n",
+       "1                   1                     3           3        3          3   \n",
+       "2                   3                     4           2        3          3   \n",
+       "3                   4                     1           5        5          5   \n",
+       "4                   2                     3           5        5          4   \n",
+       "5                   3                     3           5        5          5   \n",
+       "6                   5                     2           5        4          4   \n",
+       "7                   5                     4           3        2          2   \n",
+       "8                   4                     2           3        4          3   \n",
+       "9                   5                     1           5        5          5   \n",
+       "\n",
+       "   future_insecurity                           stress_relief_activities  \n",
+       "0                  2  Religious Activities, Social Connections, Onli...  \n",
+       "1                  4                               Online Entertainment  \n",
+       "2                  1  Religious Activities, Sports and Fitness, Onli...  \n",
+       "3                  3                               Online Entertainment  \n",
+       "4                  4                               Online Entertainment  \n",
+       "5                  5  Religious Activities, Social Connections, Onli...  \n",
+       "6                  5           Social Connections, Online Entertainment  \n",
+       "7                  4  Religious Activities, Social Connections, Onli...  \n",
+       "8                  5  Religious Activities, Social Connections, Onli...  \n",
+       "9                  5                                 Sports and Fitness  \n",
+       "\n",
+       "[10 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(87, 21)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#quantidade de linha e colunas\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#cópia do DataFrame \n",
+    "#backup\n",
+    "df_backup =df.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gender                      0\n",
+      "age                         0\n",
+      "university                  0\n",
+      "degree_level                0\n",
+      "degree_major                0\n",
+      "academic_year               0\n",
+      "cgpa                        0\n",
+      "residential_status          0\n",
+      "campus_discrimination       0\n",
+      "sports_engagement           0\n",
+      "average_sleep               0\n",
+      "study_satisfaction          0\n",
+      "academic_workload           0\n",
+      "academic_pressure           0\n",
+      "financial_concerns          0\n",
+      "social_relationships        0\n",
+      "depression                  0\n",
+      "anxiety                     0\n",
+      "isolation                   0\n",
+      "future_insecurity           0\n",
+      "stress_relief_activities    0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#contar dados nulos em cada coluna\n",
+    "nulos_por_colunas = df.isnull().sum()\n",
+    "print(nulos_por_colunas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "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>age</th>\n",
+       "      <th>study_satisfaction</th>\n",
+       "      <th>academic_workload</th>\n",
+       "      <th>academic_pressure</th>\n",
+       "      <th>financial_concerns</th>\n",
+       "      <th>social_relationships</th>\n",
+       "      <th>depression</th>\n",
+       "      <th>anxiety</th>\n",
+       "      <th>isolation</th>\n",
+       "      <th>future_insecurity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>87.000000</td>\n",
+       "      <td>87.000000</td>\n",
+       "      <td>87.000000</td>\n",
+       "      <td>87.000000</td>\n",
+       "      <td>87.000000</td>\n",
+       "      <td>87.000000</td>\n",
+       "      <td>87.000000</td>\n",
+       "      <td>87.000000</td>\n",
+       "      <td>87.000000</td>\n",
+       "      <td>87.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>19.942529</td>\n",
+       "      <td>3.931034</td>\n",
+       "      <td>3.885057</td>\n",
+       "      <td>3.781609</td>\n",
+       "      <td>3.390805</td>\n",
+       "      <td>2.781609</td>\n",
+       "      <td>3.218391</td>\n",
+       "      <td>3.218391</td>\n",
+       "      <td>3.241379</td>\n",
+       "      <td>3.011494</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.623636</td>\n",
+       "      <td>1.043174</td>\n",
+       "      <td>0.854880</td>\n",
+       "      <td>1.125035</td>\n",
+       "      <td>1.400634</td>\n",
+       "      <td>1.175578</td>\n",
+       "      <td>1.367609</td>\n",
+       "      <td>1.297809</td>\n",
+       "      <td>1.405682</td>\n",
+       "      <td>1.385089</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>4.500000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>4.500000</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>26.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             age  study_satisfaction  academic_workload   academic_pressure  \\\n",
+       "count  87.000000           87.000000           87.000000          87.000000   \n",
+       "mean   19.942529            3.931034            3.885057           3.781609   \n",
+       "std     1.623636            1.043174            0.854880           1.125035   \n",
+       "min    17.000000            1.000000            2.000000           1.000000   \n",
+       "25%    19.000000            3.000000            3.000000           3.000000   \n",
+       "50%    20.000000            4.000000            4.000000           4.000000   \n",
+       "75%    21.000000            5.000000            4.500000           5.000000   \n",
+       "max    26.000000            5.000000            5.000000           5.000000   \n",
+       "\n",
+       "       financial_concerns  social_relationships  depression    anxiety  \\\n",
+       "count           87.000000             87.000000   87.000000  87.000000   \n",
+       "mean             3.390805              2.781609    3.218391   3.218391   \n",
+       "std              1.400634              1.175578    1.367609   1.297809   \n",
+       "min              1.000000              1.000000    1.000000   1.000000   \n",
+       "25%              2.500000              2.000000    2.000000   2.000000   \n",
+       "50%              3.000000              3.000000    3.000000   3.000000   \n",
+       "75%              5.000000              4.000000    4.000000   4.000000   \n",
+       "max              5.000000              5.000000    5.000000   5.000000   \n",
+       "\n",
+       "       isolation  future_insecurity  \n",
+       "count  87.000000          87.000000  \n",
+       "mean    3.241379           3.011494  \n",
+       "std     1.405682           1.385089  \n",
+       "min     1.000000           1.000000  \n",
+       "25%     2.000000           2.000000  \n",
+       "50%     3.000000           3.000000  \n",
+       "75%     4.500000           4.000000  \n",
+       "max     5.000000           5.000000  "
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe() #uma visão geral estatística das colunas numéricas de um DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [gender, age, university, degree_level, degree_major, academic_year, cgpa, residential_status, campus_discrimination, sports_engagement, average_sleep, study_satisfaction, academic_workload , academic_pressure, financial_concerns, social_relationships, depression, anxiety, isolation, future_insecurity, stress_relief_activities]\n",
+      "Index: []\n",
+      "\n",
+      "[0 rows x 21 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Verificar duplicatas\n",
+    "duplicates = df[df.duplicated()]\n",
+    "print(duplicates)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# renomear as colunas\n",
+    "df.rename(columns={\n",
+    "    'gender': 'genero',\n",
+    "    'age':'idade',\n",
+    "    'university': 'universidade',\n",
+    "    'degree_level': 'nivel_do_curso',\t\n",
+    "    'degree_major': 'area_de_concentracao',\n",
+    "    'academic_year': 'ano_academico',\n",
+    "    'cgpa': 'média_cumulativa_pontos',\t\n",
+    "    'residential_status': 'status_residencial',\t \n",
+    "    'campus_discrimination': 'discriminacao_campus',\n",
+    "    'sports_engagement': 'envolvimento_esportes',\t\n",
+    "    'average_sleep'\t: 'sono_medio',\n",
+    "    'study_satisfaction': 'satisfacao_estudos',\n",
+    "    'academic_workload ': 'carga_trabalho_academica',\t\n",
+    "    'academic_pressure'\t: 'pressao_academica',\n",
+    "    'financial_concerns': 'preocupacoes_financeiras',\t\n",
+    "    'social_relationships': 'relacionamentos_sociais',\n",
+    "    'depression\t': 'depressao',\n",
+    "    'anxiety': 'ansiedade',\t\n",
+    "    'isolation': 'isolamento',\n",
+    "    'future_insecurity': 'insegurança_futuro',\t\n",
+    "    'stress_relief_activities': 'atividades_alivio_estresse'\n",
+    "}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['genero', 'idade', 'universidade', 'nivel_do_curso',\n",
+       "       'area_de_concentracao', 'ano_academico', 'média_cumulativa_pontos',\n",
+       "       'status_residencial', 'discriminacao_campus', 'envolvimento_esportes',\n",
+       "       'sono_medio', 'satisfacao_estudos', 'carga_trabalho_academica',\n",
+       "       'pressao_academica', 'preocupacoes_financeiras',\n",
+       "       'relacionamentos_sociais', 'depression', 'ansiedade', 'isolamento',\n",
+       "       'insegurança_futuro', 'atividades_alivio_estresse'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#salvar no csv\n",
+    "df.to_csv('MentalHealthSurvey_tratado.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "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>genero</th>\n",
+       "      <th>idade</th>\n",
+       "      <th>universidade</th>\n",
+       "      <th>nivel_do_curso</th>\n",
+       "      <th>area_de_concentracao</th>\n",
+       "      <th>ano_academico</th>\n",
+       "      <th>média_cumulativa_pontos</th>\n",
+       "      <th>status_residencial</th>\n",
+       "      <th>discriminacao_campus</th>\n",
+       "      <th>envolvimento_esportes</th>\n",
+       "      <th>...</th>\n",
+       "      <th>satisfacao_estudos</th>\n",
+       "      <th>carga_trabalho_academica</th>\n",
+       "      <th>pressao_academica</th>\n",
+       "      <th>preocupacoes_financeiras</th>\n",
+       "      <th>relacionamentos_sociais</th>\n",
+       "      <th>depression</th>\n",
+       "      <th>ansiedade</th>\n",
+       "      <th>isolamento</th>\n",
+       "      <th>insegurança_futuro</th>\n",
+       "      <th>atividades_alivio_estresse</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>20</td>\n",
+       "      <td>PU</td>\n",
+       "      <td>Undergraduate</td>\n",
+       "      <td>Data Science</td>\n",
+       "      <td>2nd year</td>\n",
+       "      <td>3.0-3.5</td>\n",
+       "      <td>Off-Campus</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No Sports</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Religious Activities, Social Connections, Onli...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>20</td>\n",
+       "      <td>UET</td>\n",
+       "      <td>Postgraduate</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>3rd year</td>\n",
+       "      <td>3.0-3.5</td>\n",
+       "      <td>Off-Campus</td>\n",
+       "      <td>No</td>\n",
+       "      <td>1-3 times</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Online Entertainment</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>20</td>\n",
+       "      <td>FAST</td>\n",
+       "      <td>Undergraduate</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>3rd year</td>\n",
+       "      <td>2.5-3.0</td>\n",
+       "      <td>Off-Campus</td>\n",
+       "      <td>No</td>\n",
+       "      <td>1-3 times</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Religious Activities, Sports and Fitness, Onli...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>20</td>\n",
+       "      <td>UET</td>\n",
+       "      <td>Undergraduate</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>3rd year</td>\n",
+       "      <td>2.5-3.0</td>\n",
+       "      <td>On-Campus</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No Sports</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Online Entertainment</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>20</td>\n",
+       "      <td>UET</td>\n",
+       "      <td>Undergraduate</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>3rd year</td>\n",
+       "      <td>3.0-3.5</td>\n",
+       "      <td>Off-Campus</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No Sports</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Online Entertainment</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>20</td>\n",
+       "      <td>UET</td>\n",
+       "      <td>Undergraduate</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>3rd year</td>\n",
+       "      <td>3.0-3.5</td>\n",
+       "      <td>Off-Campus</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No Sports</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Religious Activities, Social Connections, Onli...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>26</td>\n",
+       "      <td>PU</td>\n",
+       "      <td>Postgraduate</td>\n",
+       "      <td>Data Science</td>\n",
+       "      <td>1st year</td>\n",
+       "      <td>2.5-3.0</td>\n",
+       "      <td>On-Campus</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>1-3 times</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Social Connections, Online Entertainment</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>22</td>\n",
+       "      <td>PU</td>\n",
+       "      <td>Undergraduate</td>\n",
+       "      <td>Data Science</td>\n",
+       "      <td>2nd year</td>\n",
+       "      <td>3.0-3.5</td>\n",
+       "      <td>Off-Campus</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No Sports</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Religious Activities, Social Connections, Onli...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>20</td>\n",
+       "      <td>COMSATS</td>\n",
+       "      <td>Undergraduate</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>3rd year</td>\n",
+       "      <td>2.5-3.0</td>\n",
+       "      <td>Off-Campus</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>1-3 times</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Religious Activities, Social Connections, Onli...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>23</td>\n",
+       "      <td>COMSATS</td>\n",
+       "      <td>Undergraduate</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>3rd year</td>\n",
+       "      <td>2.5-3.0</td>\n",
+       "      <td>Off-Campus</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No Sports</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Sports and Fitness</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   genero  idade universidade nivel_do_curso area_de_concentracao  \\\n",
+       "0    Male     20           PU  Undergraduate         Data Science   \n",
+       "1    Male     20          UET   Postgraduate     Computer Science   \n",
+       "2    Male     20         FAST  Undergraduate     Computer Science   \n",
+       "3    Male     20          UET  Undergraduate     Computer Science   \n",
+       "4  Female     20          UET  Undergraduate     Computer Science   \n",
+       "5  Female     20          UET  Undergraduate     Computer Science   \n",
+       "6    Male     26           PU   Postgraduate         Data Science   \n",
+       "7    Male     22           PU  Undergraduate         Data Science   \n",
+       "8    Male     20      COMSATS  Undergraduate     Computer Science   \n",
+       "9    Male     23      COMSATS  Undergraduate     Computer Science   \n",
+       "\n",
+       "  ano_academico média_cumulativa_pontos status_residencial  \\\n",
+       "0      2nd year                 3.0-3.5         Off-Campus   \n",
+       "1      3rd year                 3.0-3.5         Off-Campus   \n",
+       "2      3rd year                 2.5-3.0         Off-Campus   \n",
+       "3      3rd year                 2.5-3.0          On-Campus   \n",
+       "4      3rd year                 3.0-3.5         Off-Campus   \n",
+       "5      3rd year                 3.0-3.5         Off-Campus   \n",
+       "6      1st year                 2.5-3.0          On-Campus   \n",
+       "7      2nd year                 3.0-3.5         Off-Campus   \n",
+       "8      3rd year                 2.5-3.0         Off-Campus   \n",
+       "9      3rd year                 2.5-3.0         Off-Campus   \n",
+       "\n",
+       "  discriminacao_campus envolvimento_esportes  ... satisfacao_estudos  \\\n",
+       "0                   No             No Sports  ...                  5   \n",
+       "1                   No             1-3 times  ...                  5   \n",
+       "2                   No             1-3 times  ...                  5   \n",
+       "3                   No             No Sports  ...                  3   \n",
+       "4                  Yes             No Sports  ...                  3   \n",
+       "5                   No             No Sports  ...                  4   \n",
+       "6                  Yes             1-3 times  ...                  4   \n",
+       "7                  Yes             No Sports  ...                  3   \n",
+       "8                  Yes             1-3 times  ...                  3   \n",
+       "9                   No             No Sports  ...                  3   \n",
+       "\n",
+       "   carga_trabalho_academica  pressao_academica  preocupacoes_financeiras  \\\n",
+       "0                         4                  5                         4   \n",
+       "1                         4                  4                         1   \n",
+       "2                         5                  5                         3   \n",
+       "3                         5                  4                         4   \n",
+       "4                         5                  5                         2   \n",
+       "5                         5                  5                         3   \n",
+       "6                         4                  4                         5   \n",
+       "7                         4                  4                         5   \n",
+       "8                         4                  3                         4   \n",
+       "9                         5                  3                         5   \n",
+       "\n",
+       "   relacionamentos_sociais  depression  ansiedade  isolamento  \\\n",
+       "0                        3           2          1           1   \n",
+       "1                        3           3          3           3   \n",
+       "2                        4           2          3           3   \n",
+       "3                        1           5          5           5   \n",
+       "4                        3           5          5           4   \n",
+       "5                        3           5          5           5   \n",
+       "6                        2           5          4           4   \n",
+       "7                        4           3          2           2   \n",
+       "8                        2           3          4           3   \n",
+       "9                        1           5          5           5   \n",
+       "\n",
+       "   insegurança_futuro                         atividades_alivio_estresse  \n",
+       "0                   2  Religious Activities, Social Connections, Onli...  \n",
+       "1                   4                               Online Entertainment  \n",
+       "2                   1  Religious Activities, Sports and Fitness, Onli...  \n",
+       "3                   3                               Online Entertainment  \n",
+       "4                   4                               Online Entertainment  \n",
+       "5                   5  Religious Activities, Social Connections, Onli...  \n",
+       "6                   5           Social Connections, Online Entertainment  \n",
+       "7                   4  Religious Activities, Social Connections, Onli...  \n",
+       "8                   5  Religious Activities, Social Connections, Onli...  \n",
+       "9                   5                                 Sports and Fitness  \n",
+       "\n",
+       "[10 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(10)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import ttest_ind\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Qual é a distribuição de frequências das universidades no dataset?\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.hist(df['universidade'].dropna(), bins=10, color='green', edgecolor='black')\n",
+    "\n",
+    "plt.title('Distribuição de Universidades')\n",
+    "plt.xlabel('Universidade')\n",
+    "plt.ylabel('Contagem')\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Como os diferentes níveis de curso estão distribuídos entre as universidades?\n",
+    "\n",
+    "# Agrupar e contar as ocorrências\n",
+    "contagem = df.groupby(['universidade', 'nivel_do_curso']).size().unstack(fill_value=0)\n",
+    "\n",
+    "# Configurar o gráfico\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "contagem.plot(kind='bar', stacked=True, colormap='Pastel1', edgecolor='black')\n",
+    "\n",
+    "plt.title('Nível do Curso por Universidade')\n",
+    "plt.xlabel('Universidade')\n",
+    "plt.ylabel('Contagem')\n",
+    "\n",
+    "plt.legend(title='Nível do Curso')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Os estudantes relatam ter sofrido discriminação no campus?\n",
+    "# Contar as ocorrências de cada resposta (Verdadeiro ou Falso)\n",
+    "contagem = df['discriminacao_campus'].value_counts()\n",
+    "\n",
+    "# Configurar o gráfico de pizza\n",
+    "plt.figure(figsize=(6, 6))\n",
+    "plt.pie(contagem, labels=contagem.index, autopct='%1.1f%%', colors=['lightpink', 'lightskyblue'], startangle=140)\n",
+    "\n",
+    "plt.title('Distribuição de Respostas sobre Discriminação no Campus')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "#Qual é a distribuição de gênero entre os estudantes no dataset?\n",
+    "\n",
+    "\n",
+    "# Contar o número de homens e mulheres\n",
+    "n_homens = len(df[df['genero'] == 'Male'])\n",
+    "n_mulheres = len(df[df['genero'] == 'Female'])\n",
+    "\n",
+    "# Dados para o gráfico\n",
+    "gêneros = ['Homens', 'Mulheres']\n",
+    "contagens = [n_homens, n_mulheres]\n",
+    "\n",
+    "# Criar o gráfico\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "plt.barh(gêneros, contagens, color=['blue', 'pink'])\n",
+    "\n",
+    "# Adicionar título e rótulos\n",
+    "plt.title('Distribuição de Gênero no Dataset')\n",
+    "plt.xlabel('Número de Estudantes')\n",
+    "plt.ylabel('Gênero')\n",
+    "\n",
+    "# Adicionar anotações com contagens\n",
+    "for i, valor in enumerate(contagens):\n",
+    "    plt.text(valor + 0.5, i, str(valor), va='center')\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sqlite3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  envolvimento_esportes  total_estudantes  \\\n",
+      "0             1-3 times                24   \n",
+      "1             4-6 times                11   \n",
+      "2              7+ times                10   \n",
+      "3             No Sports                42   \n",
+      "\n",
+      "                                          atividades  \n",
+      "0  Online Entertainment,Religious Activities, Spo...  \n",
+      "1  Sports and Fitness, Creative Outlets, Online E...  \n",
+      "2  Religious Activities, Sports and Fitness, Soci...  \n",
+      "3  Religious Activities, Social Connections, Onli...  \n"
+     ]
+    }
+   ],
+   "source": [
+    "#Número de Estudantes que Participam de Atividades Físicas e o Tipo de Atividades\n",
+    "# conexão \n",
+    "conn = sqlite3.connect(':memory:')\n",
+    "#escrever o df em uma tabela sql\n",
+    "df.to_sql('df', conn, index=False, if_exists='replace') \n",
+    "\n",
+    "#executar a consulta \n",
+    "query_sql = \"\"\"\n",
+    "SELECT envolvimento_esportes, COUNT(*) AS total_estudantes, GROUP_CONCAT(DISTINCT atividades_alivio_estresse) AS atividades\n",
+    "FROM df\n",
+    "GROUP BY envolvimento_esportes;\n",
+    "\"\"\"\n",
+    "crescimento_P = pd.read_sql_query(query_sql, conn)\n",
+    "print(crescimento_P )\n",
+    "\n",
+    "#fechar a conexão\n",
+    "conn.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  nivel_do_curso  media_ansiedade  media_depressao\n",
+      "0   Postgraduate         3.500000              4.0\n",
+      "1  Undergraduate         3.211765              3.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Média de Ansiedade e Depressão por Nível de Curso\n",
+    "\n",
+    "\n",
+    "# conexão \n",
+    "conn = sqlite3.connect(':memory:')\n",
+    "#escrever o df em uma tabela sql\n",
+    "df.to_sql('df', conn, index=False, if_exists='replace') \n",
+    "\n",
+    "#executar a consulta \n",
+    "query_sql = \"\"\"\n",
+    "SELECT nivel_do_curso, AVG(ansiedade) AS media_ansiedade, AVG(depression) AS media_depressao\n",
+    "FROM df\n",
+    "GROUP BY nivel_do_curso;\n",
+    "\"\"\"\n",
+    "crescimento_P = pd.read_sql_query(query_sql, conn)\n",
+    "print(crescimento_P )\n",
+    "\n",
+    "#fechar a conexão\n",
+    "conn.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from scipy import stats\n",
+    "import seaborn as sns\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Estatística t: -1.4691467730618726\n",
+      "Valor p: 0.1454844501013555\n",
+      "Falhamos em rejeitar a hipótese nula. Não há uma diferença significativa na ansiedade entre estudantes que fazem atividades físicas e aqueles que não fazem.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Há uma diferença significativa nos níveis de ansiedade entre estudantes que praticam atividades físicas (envolvimento em esportes) e aqueles que não praticam?\n",
+    "\n",
+    "# Dividir os dados em dois grupos\n",
+    "com_esportes = df[df['envolvimento_esportes'] != 'No Sports']['ansiedade']\n",
+    "sem_esportes = df[df['envolvimento_esportes'] == 'No Sports']['ansiedade']\n",
+    "\n",
+    "# Realizar o teste t\n",
+    "t_stat, p_value = stats.ttest_ind(com_esportes, sem_esportes)\n",
+    "\n",
+    "# Exibir os resultados\n",
+    "print(f'Estatística t: {t_stat}')\n",
+    "print(f'Valor p: {p_value}')\n",
+    "\n",
+    "# Verificar se rejeitamos a hipótese nula\n",
+    "alpha = 0.05\n",
+    "if p_value < alpha:\n",
+    "    print(\"Rejeitamos a hipótese nula. Há uma diferença significativa na ansiedade entre estudantes que fazem atividades físicas e aqueles que não fazem.\")\n",
+    "else:\n",
+    "    print(\"Falhamos em rejeitar a hipótese nula. Não há uma diferença significativa na ansiedade entre estudantes que fazem atividades físicas e aqueles que não fazem.\")\n",
+    "\n",
+    "\n",
+    "# Criar o gráfico de boxplot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.boxplot(x='envolvimento_esportes', y='ansiedade', data=df)\n",
+    "plt.title('Distribuição da Ansiedade por Envolvimento em Esportes')\n",
+    "plt.xlabel('Envolvimento em Esportes')\n",
+    "plt.ylabel('Ansiedade')\n",
+    "#plt.xticks(rotation=45)  # Rotaciona os rótulos do eixo x para melhor visualização\n",
+    "plt.show()\n",
+    "\n",
+    "# Mediana: A linha dentro de cada caixa representa a mediana dos dados, permitindo uma comparação direta dos níveis de ansiedade entre os dois grupos.\n",
+    "\n",
+    "# Dispersão: A altura das caixas mostra a variação dos dados dentro de cada grupo, indicando a consistência ou variabilidade dos níveis de ansiedade.\n",
+    "\n",
+    "# Outliers: O boxplot também facilita a identificação de outliers, que são valores atípicos que podem influenciar a análise.\n",
+    "\n",
+    "\n",
+    "\n",
+    "    "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercicios/para-casa/Ladiane/MentalHealthSurvey.csv b/exercicios/para-casa/Ladiane/MentalHealthSurvey.csv
new file mode 100644
index 0000000..fe21bdf
--- /dev/null
+++ b/exercicios/para-casa/Ladiane/MentalHealthSurvey.csv
@@ -0,0 +1,88 @@
+gender,age,university,degree_level,degree_major,academic_year,cgpa,residential_status,campus_discrimination,sports_engagement,average_sleep,study_satisfaction,academic_workload ,academic_pressure,financial_concerns,social_relationships,depression,anxiety,isolation,future_insecurity,stress_relief_activities
+Male,20,PU,Undergraduate,Data Science,2nd year,3.0-3.5,Off-Campus,No,No Sports,4-6 hrs,5,4,5,4,3,2,1,1,2,"Religious Activities, Social Connections, Online Entertainment"
+Male,20,UET,Postgraduate,Computer Science,3rd year,3.0-3.5,Off-Campus,No,1-3 times,4-6 hrs,5,4,4,1,3,3,3,3,4,Online Entertainment
+Male,20,FAST,Undergraduate,Computer Science,3rd year,2.5-3.0,Off-Campus,No,1-3 times,2-4 hrs,5,5,5,3,4,2,3,3,1,"Religious Activities, Sports and Fitness, Online Entertainment"
+Male,20,UET,Undergraduate,Computer Science,3rd year,2.5-3.0,On-Campus,No,No Sports,4-6 hrs,3,5,4,4,1,5,5,5,3,Online Entertainment
+Female,20,UET,Undergraduate,Computer Science,3rd year,3.0-3.5,Off-Campus,Yes,No Sports,4-6 hrs,3,5,5,2,3,5,5,4,4,Online Entertainment
+Female,20,UET,Undergraduate,Computer Science,3rd year,3.0-3.5,Off-Campus,No,No Sports,4-6 hrs,4,5,5,3,3,5,5,5,5,"Religious Activities, Social Connections, Online Entertainment"
+Male,26,PU,Postgraduate,Data Science,1st year,2.5-3.0,On-Campus,Yes,1-3 times,7-8 hrs,4,4,4,5,2,5,4,4,5,"Social Connections, Online Entertainment"
+Male,22,PU,Undergraduate,Data Science,2nd year,3.0-3.5,Off-Campus,Yes,No Sports,4-6 hrs,3,4,4,5,4,3,2,2,4,"Religious Activities, Social Connections, Online Entertainment"
+Male,20,COMSATS,Undergraduate,Computer Science,3rd year,2.5-3.0,Off-Campus,Yes,1-3 times,4-6 hrs,3,4,3,4,2,3,4,3,5,"Religious Activities, Social Connections, Online Entertainment, Outdoor Activities"
+Male,23,COMSATS,Undergraduate,Computer Science,3rd year,2.5-3.0,Off-Campus,No,No Sports,4-6 hrs,3,5,3,5,1,5,5,5,5,Sports and Fitness
+Male,20,COMSATS,Undergraduate,Computer Science,2nd year,3.0-3.5,On-Campus,No,No Sports,4-6 hrs,5,4,4,1,4,3,3,1,3,"Religious Activities, Online Entertainment"
+Male,20,COMSATS,Undergraduate,Computer Science,3rd year,3.0-3.5,Off-Campus,No,1-3 times,4-6 hrs,4,5,4,4,4,2,3,1,2,"Religious Activities, Sports and Fitness, Social Connections, Online Entertainment, Outdoor Activities"
+Male,21,COMSATS,Undergraduate,Computer Science,3rd year,3.5-4.0,On-Campus,No,No Sports,4-6 hrs,5,3,5,5,1,4,4,4,2,Nothing
+Male,19,PU,Undergraduate,Data Science,1st year,3.0-3.5,Off-Campus,No,No Sports,4-6 hrs,3,3,4,5,3,5,4,5,1,Religious Activities
+Female,20,PU,Undergraduate,Data Science,1st year,3.0-3.5,Off-Campus,No,No Sports,4-6 hrs,3,3,3,3,2,4,3,5,3,Religious Activities
+Female,19,PU,Undergraduate,Data Science,1st year,3.0-3.5,Off-Campus,Yes,No Sports,7-8 hrs,5,3,3,4,3,1,1,1,3,"Religious Activities, Social Connections, Online Entertainment"
+Female,20,PU,Undergraduate,Data Science,1st year,3.0-3.5,Off-Campus,No,No Sports,2-4 hrs,5,4,4,3,1,3,2,3,1,Social Connections
+Male,19,PU,Undergraduate,Data Science,1st year,2.5-3.0,Off-Campus,No,No Sports,7-8 hrs,5,3,5,3,3,2,2,3,1,Religious Activities
+Female,19,PU,Undergraduate,Data Science,1st year,3.5-4.0,Off-Campus,No,1-3 times,4-6 hrs,4,4,3,3,2,4,4,3,2,"Religious Activities, Creative Outlets, Social Connections"
+Male,19,PU,Undergraduate,Data Science,1st year,3.5-4.0,Off-Campus,Yes,1-3 times,4-6 hrs,5,4,4,5,3,5,5,4,3,Sports and Fitness
+Female,20,PU,Undergraduate,Data Science,1st year,3.0-3.5,Off-Campus,No,1-3 times,7-8 hrs,5,4,4,2,3,1,2,2,2,Social Connections
+Female,19,PU,Undergraduate,Data Science,1st year,3.0-3.5,Off-Campus,Yes,No Sports,4-6 hrs,4,5,5,2,2,4,4,5,5,"Religious Activities, Social Connections"
+Male,20,PU,Undergraduate,Data Science,2nd year,2.5-3.0,Off-Campus,No,No Sports,4-6 hrs,3,5,5,3,3,4,4,3,4,Outdoor Activities
+Male,20,UET,Undergraduate,Computer Science,3rd year,3.5-4.0,Off-Campus,No,1-3 times,4-6 hrs,5,5,4,5,3,5,5,5,3,"Religious Activities, Online Entertainment"
+Female,19,PU,Undergraduate,Data Science,1st year,3.5-4.0,Off-Campus,No,1-3 times,4-6 hrs,5,4,4,1,3,3,4,3,3,"Religious Activities, Creative Outlets"
+Male,19,PU,Undergraduate,Data Science,1st year,3.0-3.5,Off-Campus,No,7+ times,4-6 hrs,5,5,3,5,5,4,1,3,2,"Religious Activities, Sports and Fitness, Social Connections, Online Entertainment, Outdoor Activities"
+Male,23,PU,Undergraduate,Data Science,2nd year,2.5-3.0,Off-Campus,Yes,1-3 times,7-8 hrs,3,3,4,1,4,3,3,2,3,"Religious Activities, Online Entertainment, Outdoor Activities"
+Male,20,PU,Undergraduate,Computer Science,1st year,1.5-2.0,Off-Campus,Yes,7+ times,2-4 hrs,1,4,5,5,1,5,5,5,5,Online Entertainment
+Male,18,PU,Undergraduate,Data Science,1st year,2.5-3.0,Off-Campus,No,4-6 times,4-6 hrs,4,3,4,4,4,3,4,4,2,"Sports and Fitness, Creative Outlets, Online Entertainment, Outdoor Activities"
+Male,20,PU,Undergraduate,Data Science,1st year,3.5-4.0,Off-Campus,No,4-6 times,7-8 hrs,5,5,5,3,5,5,3,3,2,"Religious Activities, Outdoor Activities"
+Male,18,PU,Undergraduate,Data Science,1st year,3.5-4.0,Off-Campus,No,No Sports,7-8 hrs,5,3,1,1,3,1,2,2,1,Sleep
+Male,19,PU,Undergraduate,Data Science,1st year,2.0-2.5,On-Campus,No,No Sports,4-6 hrs,2,4,1,5,3,3,3,3,2,"Creative Outlets, Social Connections, Online Entertainment, Outdoor Activities"
+Male,18,PU,Undergraduate,Data Science,1st year,1.5-2.0,On-Campus,No,No Sports,4-6 hrs,5,4,5,2,3,4,3,4,4,Religious Activities
+Female,19,PU,Undergraduate,Data Science,1st year,3.0-3.5,On-Campus,No,No Sports,7-8 hrs,5,4,4,3,2,4,2,3,1,Creative Outlets
+Female,17,PU,Undergraduate,Computer Science,1st year,3.5-4.0,On-Campus,No,No Sports,4-6 hrs,4,5,5,5,1,5,5,5,3,Sleep
+Male,18,PU,Undergraduate,Computer Science,1st year,3.5-4.0,Off-Campus,No,7+ times,4-6 hrs,3,3,4,5,2,4,3,4,5,"Religious Activities, Sports and Fitness, Social Connections"
+Female,17,PU,Undergraduate,Computer Science,1st year,3.5-4.0,On-Campus,No,No Sports,4-6 hrs,4,4,4,5,3,4,4,5,2,Religious Activities
+Female,19,PU,Undergraduate,Data Science,1st year,3.0-3.5,Off-Campus,No,4-6 times,7-8 hrs,5,3,2,2,4,1,2,1,1,Religious Activities
+Female,18,PU,Undergraduate,Data Science,1st year,2.5-3.0,Off-Campus,No,No Sports,4-6 hrs,1,4,5,5,2,5,5,5,5,Creative Outlets
+Female,19,PU,Undergraduate,Data Science,2nd year,2.5-3.0,On-Campus,No,No Sports,2-4 hrs,2,4,4,1,2,3,3,4,3,Religious Activities
+Female,19,PU,Undergraduate,Data Science,2nd year,2.5-3.0,Off-Campus,No,1-3 times,4-6 hrs,4,4,4,4,1,1,1,1,4,"Religious Activities, Social Connections, Online Entertainment"
+Male,18,PU,Undergraduate,Data Science,1st year,3.0-3.5,Off-Campus,No,No Sports,7-8 hrs,2,4,4,5,1,5,4,5,5,Online Entertainment
+Male,18,PU,Undergraduate,Data Science,1st year,3.5-4.0,Off-Campus,Yes,1-3 times,4-6 hrs,4,3,3,2,3,3,3,3,4,Online Entertainment
+Male,18,PU,Undergraduate,Computer Science,1st year,3.5-4.0,Off-Campus,Yes,4-6 times,7-8 hrs,4,2,2,1,3,1,1,2,3,Online Entertainment
+Male,19,NUST,Undergraduate,Computer Science,3rd year,3.5-4.0,On-Campus,No,1-3 times,7-8 hrs,4,4,4,3,4,2,2,2,4,"Religious Activities, Online Entertainment, Outdoor Activities"
+Male,18,PU,Undergraduate,Data Science,1st year,0.0-0.0,Off-Campus,No,7+ times,7-8 hrs,4,4,4,4,5,3,3,1,3,Sports and Fitness
+Male,21,PU,Undergraduate,Software Engineering,4th year,3.5-4.0,Off-Campus,Yes,1-3 times,4-6 hrs,3,4,5,4,3,5,5,5,5,"Religious Activities, Sports and Fitness, Creative Outlets, Online Entertainment"
+Male,20,PU,Undergraduate,Data Science,3rd year,3.5-4.0,Off-Campus,Yes,No Sports,7-8 hrs,4,4,3,1,1,3,3,5,2,Online Entertainment
+Male,19,PU,Undergraduate,Computer Science,1st year,2.0-2.5,On-Campus,No,1-3 times,4-6 hrs,5,4,5,5,1,3,4,5,1,"Sports and Fitness, Creative Outlets, Social Connections, Outdoor Activities, Sleep"
+Male,20,PU,Undergraduate,Data Science,2nd year,3.0-3.5,On-Campus,No,No Sports,4-6 hrs,5,3,1,5,3,1,1,2,1,Religious Activities
+Male,21,PU,Undergraduate,Data Science,2nd year,3.5-4.0,On-Campus,No,1-3 times,4-6 hrs,5,4,3,4,5,2,2,2,3,"Religious Activities, Social Connections, Online Entertainment, Sleep"
+Male,17,PU,Undergraduate,Information Technology,1st year,0.0-0.0,Off-Campus,No,7+ times,7-8 hrs,3,5,5,1,3,4,4,4,5,Outdoor Activities
+Male,18,PU,Undergraduate,Information Technology,1st year,0.0-0.0,Off-Campus,No,7+ times,7-8 hrs,5,3,4,3,5,3,3,3,1,"Religious Activities, Sports and Fitness, Social Connections"
+Male,18,PU,Undergraduate,Information Technology,1st year,0.0-0.0,Off-Campus,No,1-3 times,4-6 hrs,5,5,3,2,1,2,4,5,2,"Social Connections, Online Entertainment, Sleep"
+Male,22,VU,Undergraduate,Software Engineering,4th year,3.0-3.5,Off-Campus,No,1-3 times,7-8 hrs,4,4,3,5,1,3,3,4,1,"Religious Activities, Sports and Fitness, Outdoor Activities"
+Male,21,PU,Undergraduate,Data Science,2nd year,2.5-3.0,On-Campus,Yes,4-6 times,4-6 hrs,3,4,5,3,4,1,1,1,1,"Religious Activities, Social Connections"
+Male,20,COMSATS,Undergraduate,Computer Science,3rd year,3.5-4.0,Off-Campus,Yes,4-6 times,7-8 hrs,4,3,5,3,3,4,4,4,4,Outdoor Activities
+Male,20,PU,Undergraduate,Data Science,1st year,0.0-0.0,On-Campus,No,No Sports,4-6 hrs,4,4,4,5,2,3,2,4,2,Sleep
+Male,20,PU,Undergraduate,Data Science,2nd year,3.5-4.0,Off-Campus,No,4-6 times,7-8 hrs,5,2,2,1,3,1,1,1,1,"Religious Activities, Social Connections"
+Male,21,FAST,Undergraduate,Data Science,3rd year,2.0-2.5,Off-Campus,No,1-3 times,7-8 hrs,3,5,4,1,3,2,2,4,4,"Religious Activities, Sports and Fitness"
+Male,21,FAST,Undergraduate,Computer Science,3rd year,2.5-3.0,Off-Campus,No,4-6 times,7-8 hrs,4,4,4,2,4,2,2,2,3,Outdoor Activities
+Male,20,COMSATS,Undergraduate,Computer Science,3rd year,3.0-3.5,Off-Campus,No,1-3 times,4-6 hrs,3,4,3,4,1,1,2,1,3,"Religious Activities, Social Connections, Online Entertainment"
+Male,20,UMT,Undergraduate,Computer Science,3rd year,3.5-4.0,Off-Campus,No,7+ times,7-8 hrs,5,3,4,3,2,4,4,4,5,"Religious Activities, Sports and Fitness, Social Connections, Online Entertainment, Outdoor Activities, Sleep"
+Female,19,PU,Undergraduate,Computer Science,2nd year,3.0-3.5,Off-Campus,No,No Sports,7-8 hrs,4,3,3,3,3,4,4,3,2,"Religious Activities, Social Connections, Sleep"
+Male,20,COMSATS,Undergraduate,Computer Science,3rd year,2.5-3.0,Off-Campus,Yes,1-3 times,4-6 hrs,3,4,4,5,3,5,5,5,4,"Religious Activities, Sports and Fitness, Creative Outlets, Online Entertainment"
+Male,20,UMT,Undergraduate,Computer Science,3rd year,3.5-4.0,On-Campus,No,No Sports,7-8 hrs,3,3,4,5,3,4,4,4,4,Social Connections
+Male,20,FAST,Undergraduate,Computer Science,3rd year,2.5-3.0,Off-Campus,No,1-3 times,4-6 hrs,4,5,5,1,5,3,4,1,1,"Religious Activities, Sports and Fitness, Social Connections, Sleep"
+Male,20,FAST,Undergraduate,Computer Science,3rd year,3.5-4.0,On-Campus,No,No Sports,4-6 hrs,4,5,5,3,3,2,2,3,3,"Religious Activities, Social Connections, Online Entertainment, Sleep"
+Male,20,FAST,Undergraduate,Computer Science,3rd year,2.5-3.0,On-Campus,No,7+ times,4-6 hrs,4,5,5,5,4,3,4,2,3,"Social Connections, Sleep"
+Male,19,UOL,Undergraduate,Computer Science,3rd year,2.5-3.0,Off-Campus,Yes,4-6 times,7-8 hrs,3,2,4,4,4,1,1,3,3,Outdoor Activities
+Male,22,UET,Undergraduate,Computer Science,3rd year,2.5-3.0,Off-Campus,No,No Sports,4-6 hrs,4,4,5,4,3,4,4,4,5,Religious Activities
+Male,20,UET,Undergraduate,Computer Science,3rd year,3.0-3.5,Off-Campus,No,7+ times,7-8 hrs,4,2,3,4,2,3,3,4,3,"Religious Activities, Sports and Fitness, Sleep"
+Female,20,UET,Undergraduate,Computer Science,3rd year,3.5-4.0,Off-Campus,No,No Sports,4-6 hrs,5,4,5,4,1,4,5,5,4,Online Entertainment
+Female,19,UET,Undergraduate,Computer Science,3rd year,3.0-3.5,On-Campus,No,No Sports,4-6 hrs,3,5,5,3,3,5,5,3,5,Sleep
+Male,21,PU,Undergraduate,Data Science,1st year,3.5-4.0,Off-Campus,No,7+ times,7-8 hrs,5,3,3,2,4,1,1,2,2,"Religious Activities, Social Connections"
+Male,26,KUST,Undergraduate,Data Science,4th year,3.5-4.0,Off-Campus,Yes,1-3 times,7-8 hrs,5,5,1,5,1,5,5,3,5,"Creative Outlets, Social Connections, Online Entertainment"
+Female,19,UET,Undergraduate,Computer Science,3rd year,2.5-3.0,Off-Campus,Yes,No Sports,4-6 hrs,3,5,5,5,3,4,3,3,4,"Social Connections, Sleep"
+Male,22,PU,Undergraduate,Information Technology,4th year,3.0-3.5,Off-Campus,No,4-6 times,4-6 hrs,5,3,3,3,4,2,2,2,4,"Social Connections, Outdoor Activities"
+Male,22,PU,Undergraduate,Information Technology,4th year,3.0-3.5,Off-Campus,Yes,No Sports,4-6 hrs,5,4,2,5,1,5,5,5,3,"Religious Activities, Sports and Fitness, Online Entertainment"
+Female,21,PU,Undergraduate,Information Technology,4th year,3.5-4.0,Off-Campus,Yes,No Sports,7-8 hrs,3,3,3,3,3,5,5,5,1,Sleep
+Female,21,PU,Undergraduate,Information Technology,4th year,3.0-3.5,Off-Campus,No,No Sports,7-8 hrs,3,4,4,3,4,4,4,2,4,"Online Entertainment, Sleep"
+Male,22,PU,Undergraduate,Information Technology,4th year,2.5-3.0,Off-Campus,No,No Sports,7-8 hrs,1,2,2,1,2,2,2,5,2,Religious Activities
+Male,22,PU,Undergraduate,Information Technology,4th year,3.0-3.5,On-Campus,No,No Sports,7-8 hrs,4,3,1,4,4,2,3,1,2,"Online Entertainment, Outdoor Activities, Sleep"
+Female,22,COMSATS,Undergraduate,Software Engineering,4th year,3.5-4.0,Off-Campus,Yes,No Sports,2-4 hrs,5,5,5,5,1,5,5,5,5,"Religious Activities, Sleep"
+Male,21,PU,Undergraduate,Data Science,2nd year,2.0-2.5,Off-Campus,No,No Sports,4-6 hrs,5,4,2,4,3,1,2,1,1,"Religious Activities, Sports and Fitness, Social Connections, Online Entertainment, Outdoor Activities, Sleep"
+Male,22,PU,Undergraduate,Data Science,2nd year,2.5-3.0,Off-Campus,No,No Sports,4-6 hrs,3,3,3,3,2,3,4,4,5,Sleep
+Female,19,PU,Undergraduate,Data Science,2nd year,3.5-4.0,On-Campus,No,4-6 times,4-6 hrs,4,4,4,3,4,2,2,1,3,Sports and Fitness
diff --git a/exercicios/para-casa/Ladiane/MentalHealthSurvey_tratado.csv b/exercicios/para-casa/Ladiane/MentalHealthSurvey_tratado.csv
new file mode 100644
index 0000000..840b859
--- /dev/null
+++ b/exercicios/para-casa/Ladiane/MentalHealthSurvey_tratado.csv
@@ -0,0 +1,88 @@
+genero,idade,universidade,nivel_do_curso,area_de_concentracao,ano_academico,média_cumulativa_pontos,status_residencial,discriminacao_campus,envolvimento_esportes,sono_medio,satisfacao_estudos,carga_trabalho_academica,pressao_academica,preocupacoes_financeiras,relacionamentos_sociais,depression,ansiedade,isolamento,insegurança_futuro,atividades_alivio_estresse
+Male,20,PU,Undergraduate,Data Science,2nd year,3.0-3.5,Off-Campus,No,No Sports,4-6 hrs,5,4,5,4,3,2,1,1,2,"Religious Activities, Social Connections, Online Entertainment"
+Male,20,UET,Postgraduate,Computer Science,3rd year,3.0-3.5,Off-Campus,No,1-3 times,4-6 hrs,5,4,4,1,3,3,3,3,4,Online Entertainment
+Male,20,FAST,Undergraduate,Computer Science,3rd year,2.5-3.0,Off-Campus,No,1-3 times,2-4 hrs,5,5,5,3,4,2,3,3,1,"Religious Activities, Sports and Fitness, Online Entertainment"
+Male,20,UET,Undergraduate,Computer Science,3rd year,2.5-3.0,On-Campus,No,No Sports,4-6 hrs,3,5,4,4,1,5,5,5,3,Online Entertainment
+Female,20,UET,Undergraduate,Computer Science,3rd year,3.0-3.5,Off-Campus,Yes,No Sports,4-6 hrs,3,5,5,2,3,5,5,4,4,Online Entertainment
+Female,20,UET,Undergraduate,Computer Science,3rd year,3.0-3.5,Off-Campus,No,No Sports,4-6 hrs,4,5,5,3,3,5,5,5,5,"Religious Activities, Social Connections, Online Entertainment"
+Male,26,PU,Postgraduate,Data Science,1st year,2.5-3.0,On-Campus,Yes,1-3 times,7-8 hrs,4,4,4,5,2,5,4,4,5,"Social Connections, Online Entertainment"
+Male,22,PU,Undergraduate,Data Science,2nd year,3.0-3.5,Off-Campus,Yes,No Sports,4-6 hrs,3,4,4,5,4,3,2,2,4,"Religious Activities, Social Connections, Online Entertainment"
+Male,20,COMSATS,Undergraduate,Computer Science,3rd year,2.5-3.0,Off-Campus,Yes,1-3 times,4-6 hrs,3,4,3,4,2,3,4,3,5,"Religious Activities, Social Connections, Online Entertainment, Outdoor Activities"
+Male,23,COMSATS,Undergraduate,Computer Science,3rd year,2.5-3.0,Off-Campus,No,No Sports,4-6 hrs,3,5,3,5,1,5,5,5,5,Sports and Fitness
+Male,20,COMSATS,Undergraduate,Computer Science,2nd year,3.0-3.5,On-Campus,No,No Sports,4-6 hrs,5,4,4,1,4,3,3,1,3,"Religious Activities, Online Entertainment"
+Male,20,COMSATS,Undergraduate,Computer Science,3rd year,3.0-3.5,Off-Campus,No,1-3 times,4-6 hrs,4,5,4,4,4,2,3,1,2,"Religious Activities, Sports and Fitness, Social Connections, Online Entertainment, Outdoor Activities"
+Male,21,COMSATS,Undergraduate,Computer Science,3rd year,3.5-4.0,On-Campus,No,No Sports,4-6 hrs,5,3,5,5,1,4,4,4,2,Nothing
+Male,19,PU,Undergraduate,Data Science,1st year,3.0-3.5,Off-Campus,No,No Sports,4-6 hrs,3,3,4,5,3,5,4,5,1,Religious Activities
+Female,20,PU,Undergraduate,Data Science,1st year,3.0-3.5,Off-Campus,No,No Sports,4-6 hrs,3,3,3,3,2,4,3,5,3,Religious Activities
+Female,19,PU,Undergraduate,Data Science,1st year,3.0-3.5,Off-Campus,Yes,No Sports,7-8 hrs,5,3,3,4,3,1,1,1,3,"Religious Activities, Social Connections, Online Entertainment"
+Female,20,PU,Undergraduate,Data Science,1st year,3.0-3.5,Off-Campus,No,No Sports,2-4 hrs,5,4,4,3,1,3,2,3,1,Social Connections
+Male,19,PU,Undergraduate,Data Science,1st year,2.5-3.0,Off-Campus,No,No Sports,7-8 hrs,5,3,5,3,3,2,2,3,1,Religious Activities
+Female,19,PU,Undergraduate,Data Science,1st year,3.5-4.0,Off-Campus,No,1-3 times,4-6 hrs,4,4,3,3,2,4,4,3,2,"Religious Activities, Creative Outlets, Social Connections"
+Male,19,PU,Undergraduate,Data Science,1st year,3.5-4.0,Off-Campus,Yes,1-3 times,4-6 hrs,5,4,4,5,3,5,5,4,3,Sports and Fitness
+Female,20,PU,Undergraduate,Data Science,1st year,3.0-3.5,Off-Campus,No,1-3 times,7-8 hrs,5,4,4,2,3,1,2,2,2,Social Connections
+Female,19,PU,Undergraduate,Data Science,1st year,3.0-3.5,Off-Campus,Yes,No Sports,4-6 hrs,4,5,5,2,2,4,4,5,5,"Religious Activities, Social Connections"
+Male,20,PU,Undergraduate,Data Science,2nd year,2.5-3.0,Off-Campus,No,No Sports,4-6 hrs,3,5,5,3,3,4,4,3,4,Outdoor Activities
+Male,20,UET,Undergraduate,Computer Science,3rd year,3.5-4.0,Off-Campus,No,1-3 times,4-6 hrs,5,5,4,5,3,5,5,5,3,"Religious Activities, Online Entertainment"
+Female,19,PU,Undergraduate,Data Science,1st year,3.5-4.0,Off-Campus,No,1-3 times,4-6 hrs,5,4,4,1,3,3,4,3,3,"Religious Activities, Creative Outlets"
+Male,19,PU,Undergraduate,Data Science,1st year,3.0-3.5,Off-Campus,No,7+ times,4-6 hrs,5,5,3,5,5,4,1,3,2,"Religious Activities, Sports and Fitness, Social Connections, Online Entertainment, Outdoor Activities"
+Male,23,PU,Undergraduate,Data Science,2nd year,2.5-3.0,Off-Campus,Yes,1-3 times,7-8 hrs,3,3,4,1,4,3,3,2,3,"Religious Activities, Online Entertainment, Outdoor Activities"
+Male,20,PU,Undergraduate,Computer Science,1st year,1.5-2.0,Off-Campus,Yes,7+ times,2-4 hrs,1,4,5,5,1,5,5,5,5,Online Entertainment
+Male,18,PU,Undergraduate,Data Science,1st year,2.5-3.0,Off-Campus,No,4-6 times,4-6 hrs,4,3,4,4,4,3,4,4,2,"Sports and Fitness, Creative Outlets, Online Entertainment, Outdoor Activities"
+Male,20,PU,Undergraduate,Data Science,1st year,3.5-4.0,Off-Campus,No,4-6 times,7-8 hrs,5,5,5,3,5,5,3,3,2,"Religious Activities, Outdoor Activities"
+Male,18,PU,Undergraduate,Data Science,1st year,3.5-4.0,Off-Campus,No,No Sports,7-8 hrs,5,3,1,1,3,1,2,2,1,Sleep
+Male,19,PU,Undergraduate,Data Science,1st year,2.0-2.5,On-Campus,No,No Sports,4-6 hrs,2,4,1,5,3,3,3,3,2,"Creative Outlets, Social Connections, Online Entertainment, Outdoor Activities"
+Male,18,PU,Undergraduate,Data Science,1st year,1.5-2.0,On-Campus,No,No Sports,4-6 hrs,5,4,5,2,3,4,3,4,4,Religious Activities
+Female,19,PU,Undergraduate,Data Science,1st year,3.0-3.5,On-Campus,No,No Sports,7-8 hrs,5,4,4,3,2,4,2,3,1,Creative Outlets
+Female,17,PU,Undergraduate,Computer Science,1st year,3.5-4.0,On-Campus,No,No Sports,4-6 hrs,4,5,5,5,1,5,5,5,3,Sleep
+Male,18,PU,Undergraduate,Computer Science,1st year,3.5-4.0,Off-Campus,No,7+ times,4-6 hrs,3,3,4,5,2,4,3,4,5,"Religious Activities, Sports and Fitness, Social Connections"
+Female,17,PU,Undergraduate,Computer Science,1st year,3.5-4.0,On-Campus,No,No Sports,4-6 hrs,4,4,4,5,3,4,4,5,2,Religious Activities
+Female,19,PU,Undergraduate,Data Science,1st year,3.0-3.5,Off-Campus,No,4-6 times,7-8 hrs,5,3,2,2,4,1,2,1,1,Religious Activities
+Female,18,PU,Undergraduate,Data Science,1st year,2.5-3.0,Off-Campus,No,No Sports,4-6 hrs,1,4,5,5,2,5,5,5,5,Creative Outlets
+Female,19,PU,Undergraduate,Data Science,2nd year,2.5-3.0,On-Campus,No,No Sports,2-4 hrs,2,4,4,1,2,3,3,4,3,Religious Activities
+Female,19,PU,Undergraduate,Data Science,2nd year,2.5-3.0,Off-Campus,No,1-3 times,4-6 hrs,4,4,4,4,1,1,1,1,4,"Religious Activities, Social Connections, Online Entertainment"
+Male,18,PU,Undergraduate,Data Science,1st year,3.0-3.5,Off-Campus,No,No Sports,7-8 hrs,2,4,4,5,1,5,4,5,5,Online Entertainment
+Male,18,PU,Undergraduate,Data Science,1st year,3.5-4.0,Off-Campus,Yes,1-3 times,4-6 hrs,4,3,3,2,3,3,3,3,4,Online Entertainment
+Male,18,PU,Undergraduate,Computer Science,1st year,3.5-4.0,Off-Campus,Yes,4-6 times,7-8 hrs,4,2,2,1,3,1,1,2,3,Online Entertainment
+Male,19,NUST,Undergraduate,Computer Science,3rd year,3.5-4.0,On-Campus,No,1-3 times,7-8 hrs,4,4,4,3,4,2,2,2,4,"Religious Activities, Online Entertainment, Outdoor Activities"
+Male,18,PU,Undergraduate,Data Science,1st year,0.0-0.0,Off-Campus,No,7+ times,7-8 hrs,4,4,4,4,5,3,3,1,3,Sports and Fitness
+Male,21,PU,Undergraduate,Software Engineering,4th year,3.5-4.0,Off-Campus,Yes,1-3 times,4-6 hrs,3,4,5,4,3,5,5,5,5,"Religious Activities, Sports and Fitness, Creative Outlets, Online Entertainment"
+Male,20,PU,Undergraduate,Data Science,3rd year,3.5-4.0,Off-Campus,Yes,No Sports,7-8 hrs,4,4,3,1,1,3,3,5,2,Online Entertainment
+Male,19,PU,Undergraduate,Computer Science,1st year,2.0-2.5,On-Campus,No,1-3 times,4-6 hrs,5,4,5,5,1,3,4,5,1,"Sports and Fitness, Creative Outlets, Social Connections, Outdoor Activities, Sleep"
+Male,20,PU,Undergraduate,Data Science,2nd year,3.0-3.5,On-Campus,No,No Sports,4-6 hrs,5,3,1,5,3,1,1,2,1,Religious Activities
+Male,21,PU,Undergraduate,Data Science,2nd year,3.5-4.0,On-Campus,No,1-3 times,4-6 hrs,5,4,3,4,5,2,2,2,3,"Religious Activities, Social Connections, Online Entertainment, Sleep"
+Male,17,PU,Undergraduate,Information Technology,1st year,0.0-0.0,Off-Campus,No,7+ times,7-8 hrs,3,5,5,1,3,4,4,4,5,Outdoor Activities
+Male,18,PU,Undergraduate,Information Technology,1st year,0.0-0.0,Off-Campus,No,7+ times,7-8 hrs,5,3,4,3,5,3,3,3,1,"Religious Activities, Sports and Fitness, Social Connections"
+Male,18,PU,Undergraduate,Information Technology,1st year,0.0-0.0,Off-Campus,No,1-3 times,4-6 hrs,5,5,3,2,1,2,4,5,2,"Social Connections, Online Entertainment, Sleep"
+Male,22,VU,Undergraduate,Software Engineering,4th year,3.0-3.5,Off-Campus,No,1-3 times,7-8 hrs,4,4,3,5,1,3,3,4,1,"Religious Activities, Sports and Fitness, Outdoor Activities"
+Male,21,PU,Undergraduate,Data Science,2nd year,2.5-3.0,On-Campus,Yes,4-6 times,4-6 hrs,3,4,5,3,4,1,1,1,1,"Religious Activities, Social Connections"
+Male,20,COMSATS,Undergraduate,Computer Science,3rd year,3.5-4.0,Off-Campus,Yes,4-6 times,7-8 hrs,4,3,5,3,3,4,4,4,4,Outdoor Activities
+Male,20,PU,Undergraduate,Data Science,1st year,0.0-0.0,On-Campus,No,No Sports,4-6 hrs,4,4,4,5,2,3,2,4,2,Sleep
+Male,20,PU,Undergraduate,Data Science,2nd year,3.5-4.0,Off-Campus,No,4-6 times,7-8 hrs,5,2,2,1,3,1,1,1,1,"Religious Activities, Social Connections"
+Male,21,FAST,Undergraduate,Data Science,3rd year,2.0-2.5,Off-Campus,No,1-3 times,7-8 hrs,3,5,4,1,3,2,2,4,4,"Religious Activities, Sports and Fitness"
+Male,21,FAST,Undergraduate,Computer Science,3rd year,2.5-3.0,Off-Campus,No,4-6 times,7-8 hrs,4,4,4,2,4,2,2,2,3,Outdoor Activities
+Male,20,COMSATS,Undergraduate,Computer Science,3rd year,3.0-3.5,Off-Campus,No,1-3 times,4-6 hrs,3,4,3,4,1,1,2,1,3,"Religious Activities, Social Connections, Online Entertainment"
+Male,20,UMT,Undergraduate,Computer Science,3rd year,3.5-4.0,Off-Campus,No,7+ times,7-8 hrs,5,3,4,3,2,4,4,4,5,"Religious Activities, Sports and Fitness, Social Connections, Online Entertainment, Outdoor Activities, Sleep"
+Female,19,PU,Undergraduate,Computer Science,2nd year,3.0-3.5,Off-Campus,No,No Sports,7-8 hrs,4,3,3,3,3,4,4,3,2,"Religious Activities, Social Connections, Sleep"
+Male,20,COMSATS,Undergraduate,Computer Science,3rd year,2.5-3.0,Off-Campus,Yes,1-3 times,4-6 hrs,3,4,4,5,3,5,5,5,4,"Religious Activities, Sports and Fitness, Creative Outlets, Online Entertainment"
+Male,20,UMT,Undergraduate,Computer Science,3rd year,3.5-4.0,On-Campus,No,No Sports,7-8 hrs,3,3,4,5,3,4,4,4,4,Social Connections
+Male,20,FAST,Undergraduate,Computer Science,3rd year,2.5-3.0,Off-Campus,No,1-3 times,4-6 hrs,4,5,5,1,5,3,4,1,1,"Religious Activities, Sports and Fitness, Social Connections, Sleep"
+Male,20,FAST,Undergraduate,Computer Science,3rd year,3.5-4.0,On-Campus,No,No Sports,4-6 hrs,4,5,5,3,3,2,2,3,3,"Religious Activities, Social Connections, Online Entertainment, Sleep"
+Male,20,FAST,Undergraduate,Computer Science,3rd year,2.5-3.0,On-Campus,No,7+ times,4-6 hrs,4,5,5,5,4,3,4,2,3,"Social Connections, Sleep"
+Male,19,UOL,Undergraduate,Computer Science,3rd year,2.5-3.0,Off-Campus,Yes,4-6 times,7-8 hrs,3,2,4,4,4,1,1,3,3,Outdoor Activities
+Male,22,UET,Undergraduate,Computer Science,3rd year,2.5-3.0,Off-Campus,No,No Sports,4-6 hrs,4,4,5,4,3,4,4,4,5,Religious Activities
+Male,20,UET,Undergraduate,Computer Science,3rd year,3.0-3.5,Off-Campus,No,7+ times,7-8 hrs,4,2,3,4,2,3,3,4,3,"Religious Activities, Sports and Fitness, Sleep"
+Female,20,UET,Undergraduate,Computer Science,3rd year,3.5-4.0,Off-Campus,No,No Sports,4-6 hrs,5,4,5,4,1,4,5,5,4,Online Entertainment
+Female,19,UET,Undergraduate,Computer Science,3rd year,3.0-3.5,On-Campus,No,No Sports,4-6 hrs,3,5,5,3,3,5,5,3,5,Sleep
+Male,21,PU,Undergraduate,Data Science,1st year,3.5-4.0,Off-Campus,No,7+ times,7-8 hrs,5,3,3,2,4,1,1,2,2,"Religious Activities, Social Connections"
+Male,26,KUST,Undergraduate,Data Science,4th year,3.5-4.0,Off-Campus,Yes,1-3 times,7-8 hrs,5,5,1,5,1,5,5,3,5,"Creative Outlets, Social Connections, Online Entertainment"
+Female,19,UET,Undergraduate,Computer Science,3rd year,2.5-3.0,Off-Campus,Yes,No Sports,4-6 hrs,3,5,5,5,3,4,3,3,4,"Social Connections, Sleep"
+Male,22,PU,Undergraduate,Information Technology,4th year,3.0-3.5,Off-Campus,No,4-6 times,4-6 hrs,5,3,3,3,4,2,2,2,4,"Social Connections, Outdoor Activities"
+Male,22,PU,Undergraduate,Information Technology,4th year,3.0-3.5,Off-Campus,Yes,No Sports,4-6 hrs,5,4,2,5,1,5,5,5,3,"Religious Activities, Sports and Fitness, Online Entertainment"
+Female,21,PU,Undergraduate,Information Technology,4th year,3.5-4.0,Off-Campus,Yes,No Sports,7-8 hrs,3,3,3,3,3,5,5,5,1,Sleep
+Female,21,PU,Undergraduate,Information Technology,4th year,3.0-3.5,Off-Campus,No,No Sports,7-8 hrs,3,4,4,3,4,4,4,2,4,"Online Entertainment, Sleep"
+Male,22,PU,Undergraduate,Information Technology,4th year,2.5-3.0,Off-Campus,No,No Sports,7-8 hrs,1,2,2,1,2,2,2,5,2,Religious Activities
+Male,22,PU,Undergraduate,Information Technology,4th year,3.0-3.5,On-Campus,No,No Sports,7-8 hrs,4,3,1,4,4,2,3,1,2,"Online Entertainment, Outdoor Activities, Sleep"
+Female,22,COMSATS,Undergraduate,Software Engineering,4th year,3.5-4.0,Off-Campus,Yes,No Sports,2-4 hrs,5,5,5,5,1,5,5,5,5,"Religious Activities, Sleep"
+Male,21,PU,Undergraduate,Data Science,2nd year,2.0-2.5,Off-Campus,No,No Sports,4-6 hrs,5,4,2,4,3,1,2,1,1,"Religious Activities, Sports and Fitness, Social Connections, Online Entertainment, Outdoor Activities, Sleep"
+Male,22,PU,Undergraduate,Data Science,2nd year,2.5-3.0,Off-Campus,No,No Sports,4-6 hrs,3,3,3,3,2,3,4,4,5,Sleep
+Female,19,PU,Undergraduate,Data Science,2nd year,3.5-4.0,On-Campus,No,4-6 times,4-6 hrs,4,4,4,3,4,2,2,1,3,Sports and Fitness
diff --git a/exercicios/para-casa/Ladiane/README.md b/exercicios/para-casa/Ladiane/README.md
new file mode 100644
index 0000000..4340b31
--- /dev/null
+++ b/exercicios/para-casa/Ladiane/README.md
@@ -0,0 +1,224 @@
+# Projeto II: Análise de Dados de Saúde Mental
+
+## Descrição dos Dados
+
+O dataset utilizado neste projeto é intitulado "MentalHealthSurvey.csv" e contém informações sobre a saúde mental de estudantes universitários. As variáveis incluídas no dataset abrangem aspectos como gênero, idade, universidade, nível do curso, envolvimento em atividades esportivas, e questões relacionadas à saúde mental, como ansiedade, depressão e estresse. Além disso, o dataset inclui informações sobre atividades de alívio de estresse e preocupações financeiras, entre outras variáveis.
+
+## Motivo da Escolha dos Dados
+
+O motivo principal para a escolha deste dataset é a relevância crescente das questões de saúde mental no contexto acadêmico. Em um cenário onde a pressão acadêmica e o estresse são frequentemente discutidos, entender como diferentes fatores podem influenciar a saúde mental dos estudantes é crucial. 
+
+Os dados chamaram minha atenção devido a várias razões:
+
+
+## import pandas as pd 
+## import matplotlib.pyplot as plt
+
+
+
+1. Relevância Social e Acadêmica
+
+## Instalação das Bibliotecas Necessárias
+
+Para executar este projeto, você precisará instalar as seguintes bibliotecas Python. Você pode instalar essas bibliotecas utilizando o `pip`:
+
+```
+pip install pandas numpy matplotlib seaborn scipy sqlite3 
+```
+
+## Carregamento e Preparação dos Dados
+
+'''import pandas as pd 
+import numpy as np
+
+# Carregar o dataset
+df = pd.read_csv("MentalHealthSurvey.csv")
+
+# Renomear colunas
+df.rename(columns={
+    'gender': 'genero',
+    'age': 'idade',
+    'university': 'universidade',
+    'degree_level': 'nivel_do_curso',
+    'degree_major': 'area_de_concentracao',
+    'academic_year': 'ano_academico',
+    'cgpa': 'média_cumulativa_pontos',
+    'residential_status': 'status_residencial',
+    'campus_discrimination': 'discriminacao_campus',
+    'sports_engagement': 'envolvimento_esportes',
+    'average_sleep': 'sono_medio',
+    'study_satisfaction': 'satisfacao_estudos',
+    'academic_workload': 'carga_trabalho_academica',
+    'academic_pressure': 'pressao_academica',
+    'financial_concerns': 'preocupacoes_financeiras',
+    'social_relationships': 'relacionamentos_sociais',
+    'depression': 'depressao',
+    'anxiety': 'ansiedade',
+    'isolation': 'isolamento',
+    'future_insecurity': 'inseguranca_futuro',
+    'stress_relief_activities': 'atividades_alivio_estresse'
+}, inplace=True)
+
+#Salvar o DataFrame tratado
+df.to_csv('MentalHealthSurvey_tratado.csv', index=False)'''
+
+## Visualizações e Análises
+
+## Qual é a distribuição de frequências das universidades no dataset?
+
+``` 
+import matplotlib.pyplot as plt
+
+plt.figure(figsize=(10, 6))
+plt.hist(df['universidade'].dropna(), bins=10, color='green', edgecolor='black')
+
+plt.title('Distribuição de Universidades')
+plt.xlabel('Universidade')
+plt.ylabel('Contagem')
+
+plt.show()
+
+```
+
+## Como os diferentes níveis de curso estão distribuídos entre as universidades?
+
+#Agrupar e contar as ocorrências
+
+```
+contagem = df.groupby(['universidade', 'nivel_do_curso']).size().unstack(fill_value=0)
+
+#Configurar o gráfico
+plt.figure(figsize=(10, 6))
+contagem.plot(kind='bar', stacked=True, colormap='Pastel1', edgecolor='black')
+
+plt.title('Nível do Curso por Universidade')
+plt.xlabel('Universidade')
+plt.ylabel('Contagem')
+
+plt.legend(title='Nível do Curso')
+
+plt.show()   
+
+```
+
+
+## Os estudantes relatam ter sofrido discriminação no campus?
+
+````
+#Contar as ocorrências de cada resposta (Verdadeiro ou Falso)
+contagem = df['discriminacao_campus'].value_counts()
+
+#Configurar o gráfico de pizza
+plt.figure(figsize=(6, 6))
+plt.pie(contagem, labels=contagem.index, autopct='%1.1f%%', colors=['lightpink', 'lightskyblue'], startangle=140)
+
+plt.title('Distribuição de Respostas sobre Discriminação no Campus')
+plt.show()
+
+````
+
+## Qual é a distribuição de gênero entre os estudantes no dataset?
+
+
+#Contar o número de homens e mulheres
+n_homens = len(df[df['genero'] == 'Male'])
+n_mulheres = len(df[df['genero'] == 'Female'])
+
+#Dados para o gráfico
+gêneros = ['Homens', 'Mulheres']
+contagens = [n_homens, n_mulheres]
+
+#Criar o gráfico
+plt.figure(figsize=(8, 5))
+plt.barh(gêneros, contagens, color=['blue', 'pink'])
+
+#Adicionar título e rótulos
+plt.title('Distribuição de Gênero no Dataset')
+plt.xlabel('Número de Estudantes')
+plt.ylabel('Gênero')
+
+#Adicionar anotações com contagens
+for i, valor in enumerate(contagens):
+    plt.text(valor + 0.5, i, str(valor), va='center')
+
+#Exibir o gráfico
+plt.show()
+
+# SQL
+
+````
+import sqlite3
+````
+## Número de Estudantes que Participam de Atividades Físicas e o Tipo de Atividades
+
+````#conexão 
+conn = sqlite3.connect(':memory:')
+#escrever o df em uma tabela sql
+df.to_sql('df', conn, index=False, if_exists='replace') 
+
+#executar a consulta 
+query_sql = """
+SELECT envolvimento_esportes, COUNT(*) AS total_estudantes, GROUP_CONCAT(DISTINCT atividades_alivio_estresse) AS atividades
+FROM df
+GROUP BY envolvimento_esportes;
+"""
+crescimento_P = pd.read_sql_query(query_sql, conn)
+print(crescimento_P )
+
+#fechar a conexão
+conn.close()
+
+RESULTADO
+
+  envolvimento_esportes  total_estudantes  \
+0             1-3 times                24   
+1             4-6 times                11   
+2              7+ times                10   
+3             No Sports                42   
+
+                                          atividades  
+0  Online Entertainment,Religious Activities, Spo...  
+1  Sports and Fitness, Creative Outlets, Online E...  
+2  Religious Activities, Sports and Fitness, Soci...  
+3  Religious Activities, Social Connections, Onli...  
+
+
+````
+
+## Média de Ansiedade e Depressão por Nível de Curso
+
+````
+#conexão 
+conn = sqlite3.connect(':memory:')
+#escrever o df em uma tabela sql
+df.to_sql('df', conn, index=False, if_exists='replace') 
+
+#executar a consulta 
+query_sql = """
+SELECT nivel_do_curso, AVG(ansiedade) AS media_ansiedade, AVG(depression) AS media_depressao
+FROM df
+GROUP BY nivel_do_curso;
+"""
+crescimento_P = pd.read_sql_query(query_sql, conn)
+print(crescimento_P )
+
+#fechar a conexão
+conn.close()
+
+RESULTADO
+  nivel_do_curso  media_ansiedade  media_depressao
+0   Postgraduate         3.500000              4.0
+1  Undergraduate         3.211765              3.2
+
+````
+# Há uma diferença significativa nos níveis de ansiedade entre estudantes que praticam atividades físicas (envolvimento em esportes) e aqueles que não praticam?
+
+![Descrição da Imagem](testeHip.png)
+
+
+````
+Estatística t: -1.4691467730618726
+Valor p: 0.1454844501013555
+Falhamos em rejeitar a hipótese nula. Não há uma diferença significativa na ansiedade entre estudantes que fazem atividades físicas e aqueles que não fazem.
+
+````
\ No newline at end of file
diff --git a/exercicios/para-casa/Ladiane/testeHip.png b/exercicios/para-casa/Ladiane/testeHip.png
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From d1e9643850b6edc96bc7ef5c235c4c6c0bef4484 Mon Sep 17 00:00:00 2001
From: "Ladiane P.S" <aladydyljc@gmail.com>
Date: Thu, 29 Aug 2024 22:37:14 -0300
Subject: [PATCH 2/5] personalizando o README

---
 exercicios/para-casa/Ladiane/README.md        |  21 +++++++++---------
 exercicios/para-casa/Ladiane/saudemental.jpeg | Bin 0 -> 9443 bytes
 2 files changed, 11 insertions(+), 10 deletions(-)
 create mode 100644 exercicios/para-casa/Ladiane/saudemental.jpeg

diff --git a/exercicios/para-casa/Ladiane/README.md b/exercicios/para-casa/Ladiane/README.md
index 4340b31..9adfe9a 100644
--- a/exercicios/para-casa/Ladiane/README.md
+++ b/exercicios/para-casa/Ladiane/README.md
@@ -1,6 +1,8 @@
+![Descrição da Imagem](saudemental.png)
+
 # Projeto II: Análise de Dados de Saúde Mental
 
-## Descrição dos Dados
+## Descrição dos Dados 
 
 O dataset utilizado neste projeto é intitulado "MentalHealthSurvey.csv" e contém informações sobre a saúde mental de estudantes universitários. As variáveis incluídas no dataset abrangem aspectos como gênero, idade, universidade, nível do curso, envolvimento em atividades esportivas, e questões relacionadas à saúde mental, como ansiedade, depressão e estresse. Além disso, o dataset inclui informações sobre atividades de alívio de estresse e preocupações financeiras, entre outras variáveis.
 
@@ -16,8 +18,6 @@ Os dados chamaram minha atenção devido a várias razões:
 
 
 
-1. Relevância Social e Acadêmica
-
 ## Instalação das Bibliotecas Necessárias
 
 Para executar este projeto, você precisará instalar as seguintes bibliotecas Python. Você pode instalar essas bibliotecas utilizando o `pip`:
@@ -62,9 +62,9 @@ df.rename(columns={
 #Salvar o DataFrame tratado
 df.to_csv('MentalHealthSurvey_tratado.csv', index=False)'''
 
-## Visualizações e Análises
+## Visualizações e Análises 
 
-## Qual é a distribuição de frequências das universidades no dataset?
+## Qual é a distribuição de frequências das universidades no dataset? 📊
 
 ``` 
 import matplotlib.pyplot as plt
@@ -80,7 +80,7 @@ plt.show()
 
 ```
 
-## Como os diferentes níveis de curso estão distribuídos entre as universidades?
+## Como os diferentes níveis de curso estão distribuídos entre as universidades? 🎓
 
 #Agrupar e contar as ocorrências
 
@@ -102,7 +102,7 @@ plt.show()
 ```
 
 
-## Os estudantes relatam ter sofrido discriminação no campus?
+## Os estudantes relatam ter sofrido discriminação no campus? 🚸
 
 ````
 #Contar as ocorrências de cada resposta (Verdadeiro ou Falso)
@@ -117,7 +117,7 @@ plt.show()
 
 ````
 
-## Qual é a distribuição de gênero entre os estudantes no dataset?
+## Qual é a distribuição de gênero entre os estudantes no dataset? 👩‍🎓👨‍🎓
 
 
 #Contar o número de homens e mulheres
@@ -149,7 +149,7 @@ plt.show()
 ````
 import sqlite3
 ````
-## Número de Estudantes que Participam de Atividades Físicas e o Tipo de Atividades
+## Número de Estudantes que Participam de Atividades Físicas e o Tipo de Atividades 🏋️‍♂️
 
 ````#conexão 
 conn = sqlite3.connect(':memory:')
@@ -185,7 +185,7 @@ RESULTADO
 
 ````
 
-## Média de Ansiedade e Depressão por Nível de Curso
+## Média de Ansiedade e Depressão por Nível de Curso 📈
 
 ````
 #conexão 
@@ -212,6 +212,7 @@ RESULTADO
 
 ````
 # Há uma diferença significativa nos níveis de ansiedade entre estudantes que praticam atividades físicas (envolvimento em esportes) e aqueles que não praticam?
+🤔
 
 ![Descrição da Imagem](testeHip.png)
 
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HcmV?d00001


From 75f4169422ad402f2b8dc599c2056ee85f9dfc8c Mon Sep 17 00:00:00 2001
From: "Ladiane P.S" <aladydyljc@gmail.com>
Date: Thu, 29 Aug 2024 22:38:23 -0300
Subject: [PATCH 3/5] personalizando o README1

---
 exercicios/para-casa/Ladiane/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/exercicios/para-casa/Ladiane/README.md b/exercicios/para-casa/Ladiane/README.md
index 9adfe9a..dbb067a 100644
--- a/exercicios/para-casa/Ladiane/README.md
+++ b/exercicios/para-casa/Ladiane/README.md
@@ -1,4 +1,4 @@
-![Descrição da Imagem](saudemental.png)
+![Descrição da Imagem](saudemental.jpeg
 
 # Projeto II: Análise de Dados de Saúde Mental
 

From d8a7d9254e76e3a1c00e5b0ff0cc6f16e52c9f31 Mon Sep 17 00:00:00 2001
From: "Ladiane P.S" <aladydyljc@gmail.com>
Date: Thu, 29 Aug 2024 22:43:05 -0300
Subject: [PATCH 4/5] personalizando o README2

---
 exercicios/para-casa/Ladiane/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/exercicios/para-casa/Ladiane/README.md b/exercicios/para-casa/Ladiane/README.md
index dbb067a..5dcaee2 100644
--- a/exercicios/para-casa/Ladiane/README.md
+++ b/exercicios/para-casa/Ladiane/README.md
@@ -1,4 +1,4 @@
-![Descrição da Imagem](saudemental.jpeg
+![Descrição da Imagem](saudemental.jpeg)
 
 # Projeto II: Análise de Dados de Saúde Mental
 
@@ -212,7 +212,7 @@ RESULTADO
 
 ````
 # Há uma diferença significativa nos níveis de ansiedade entre estudantes que praticam atividades físicas (envolvimento em esportes) e aqueles que não praticam?
-🤔
+🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔
 
 ![Descrição da Imagem](testeHip.png)
 

From aca35f5d55934578ef5d41e0f64a08c55ef60ac2 Mon Sep 17 00:00:00 2001
From: "Ladiane P.S" <aladydyljc@gmail.com>
Date: Thu, 29 Aug 2024 22:45:18 -0300
Subject: [PATCH 5/5] personalizando o README3

---
 exercicios/para-casa/Ladiane/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/exercicios/para-casa/Ladiane/README.md b/exercicios/para-casa/Ladiane/README.md
index 5dcaee2..5b653d0 100644
--- a/exercicios/para-casa/Ladiane/README.md
+++ b/exercicios/para-casa/Ladiane/README.md
@@ -212,7 +212,7 @@ RESULTADO
 
 ````
 # Há uma diferença significativa nos níveis de ansiedade entre estudantes que praticam atividades físicas (envolvimento em esportes) e aqueles que não praticam?
-🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔
+🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔
 
 ![Descrição da Imagem](testeHip.png)