diff --git a/Business_Challenge_EDA_and_SQL_Presentation.pptx b/Business_Challenge_EDA_and_SQL_Presentation.pptx
new file mode 100644
index 0000000..60b5f91
Binary files /dev/null and b/Business_Challenge_EDA_and_SQL_Presentation.pptx differ
diff --git a/Top 100 Languages.csv b/Top 100 Languages.csv
new file mode 100644
index 0000000..ebeb48b
--- /dev/null
+++ b/Top 100 Languages.csv	
@@ -0,0 +1,101 @@
+Language,Total Speakers,Native Speakers,Origin
+English,1132366680,379007140,Indo-European
+Mandarin Chinese,1116596640,917868640,Sino-Tibetan
+Hindi,615475540,341208640,Indo-European
+Spanish,534335730,460093030,Indo-European
+French,279821930,77177210,Indo-European
+Standard Arabic,273989700,,Afro-Asiatic
+Bengali,265042480,228289600,Indo-European
+Russian,258227760,153746530,Indo-European
+Portuguese,234168620,220762620,Indo-European
+Indonesian,198733600,43364600,Austronesian
+Urdu,170208780,68622980,Indo-European
+Standard German,132176520,76090520,Indo-European
+Japanese,128350830,128229330,Japanic
+Swahili,98327740,16027740,Niger-Congo
+Marathi,95312800,83112800,Indo-European
+Telugu,93040340,82040340,Dravidian
+Western Punjabi,92725700,92725700,Indo-European
+Wu Chinese,81501290,81437890,Sino-Tibetan
+Tamil,80989130,75039130,Dravidian
+Turkish,79779360,79399060,Turkic
+Korean,77264890,77264890,Koreanic
+Vietnamese,76950770,75950770,Austronesian
+Yue Chinese,73538610,73136610,Sino-Tibetan
+Javanese,68277600,68277600,Austronesian
+Italian,67894920,64844820,Indo-European
+Egyptian Spoken Arabic,64618100,64618100,Afro-Asiatic
+Hausa,63428100,43928100,Afro-Asiatic
+Thai,60657660,20657660,Kra-Dai
+Gujarati,60588970,56408970,Indo-European
+Kannada,56463310,43563310,Dravidian
+Iranian Persian,52782160,52782160,Indo-European
+Bhojpuri,52405300,52245300,Indo-European
+Southern Min Chinese,50462190,50075190,Sino-Tibetan
+Hakka Chinese,48467490,48467490,Sino-Tibetan
+Jinyu Chinese,46900000,46900000,Sino-Tibetan
+Filipino,45000000,,Austronesian
+Burmese,42912350,32912350,Sino-Tibetan
+Polish,40378030,39713030,Indo-European
+Yoruba,39844260,37844260,Niger-Congo
+Odia,38051547,34461520,Indo-European
+Malayalam ,37829870,37134870,Dravidian
+Xiang Chinese,37300000,37300000,Sino-Tibetan
+Maithili,34085000,33890000,Indo-European
+Ukrainian,33082790,27282790,Indo-European
+Moroccan Spoken Arabic,32608700,27488700,Afro-Asiatic
+Eastern Punjabi,32601140,32600670,Indo-European
+Sunda,32400000,32400000,Austronesian
+Algerian Spoken Arabic,32387600,29387600,Afro-Asiatic
+Sundanese Spoken Arabic,31940300,31940300,Afro-Asiatic
+Nigerian Pidgin,30000000,,Indo-European
+Zulu,27779100,12079100,Niger-Congo
+Igbo,27014190,27014190,Niger-Congo
+Amharic,25880630,21880630,Afro-Asiatic
+Northern Uzbek,25164820,25164820,Turkic
+Sindhi,24615591,24615550,Indo-European
+North Levantine Spoken Arabic,24587400,24587400,Afro-Asiatic
+Nepali,24528840,15848840,Indo-European
+Romanian,24345750,24345750,Indo-European
+Tagalog,23808890,23646890,Austronesian
+Dutch,23069480,23069480,Indo-European
+Sa'idi Spoken Arabic,22400000,22400000,Afro-Asiatic
+Gan Chinese,22100000,22100000,Sino-Tibetan
+Northern Pashto,20850900,20850900,Indo-European
+Magahi,20746400,20735600,Indo-European
+Saraiki,20009000,20009000,Indo-European
+Xhosa,19183300,8183300,Niger-Congo
+Malay,19092180,16092180,Austronesian
+Khmer,17591230,16591230,Austronesian
+Afrikaans,17534580,7234580,Indo-European
+Sinhala,17287880,15287880,Indo-European
+Somali,16321530,16225930,Afro-Asiatic
+Chhattisgarhi,16300000,16300000,Indo-European
+Cebuano,15942480,15942480,Austronesian
+Mesopotamian Spoken Arabic,15655900,15655900,Afro-Asiatic
+Assamese,15329040,15328790,Indo-European
+Northeastern Thai,15000000,15000000,Kra-Dai
+Northern Kurdish,14605670,14605670,Indo-European
+Hijazi Spoken Arabic,14524500,14524500,Afro-Asiatic
+Nigerian Fulfulde,14485000,14485000,Niger-Congo
+Bavarian,14359000,14359000,Indo-European
+Bamanankan,14102320,4102320,Niger-Congo
+South Azerbaijani,13813750,13813750,Turkic
+Northern Sotho,13731000,4631000,Niger-Congo
+Setswana,13664710,5814710,Niger-Congo
+Souther Sotho,13524520,5624520,Niger-Congo
+Czech,13386850,10704850,Indo-European
+Greek,13170460,13111960,Indo-European
+Chittagonian,13000000,13000000,Indo-European
+Kazakh,12934060,12934060,Turkic
+Swedish,12804900,9654900,Indo-European
+Deccan,12800000,12800000,Indo-European
+Hungarian,12574280,12574280,Uralic
+Jula,12486000,2208000,Niger-Congo
+Sadri,12131225,5131180,Indo-European
+Kinyarwanda,12120250,12120250,Niger-Congo
+Cameroonian Pidgin,12000000,,Indo-European
+Sylheti,11800000,10300000,Indo-European
+South Levantine Spoken Arabic,11601100,11601100,Afro-Asiatic
+Tunisian Spoken Arabic,11571600,11571600,Afro-Asiatic
+Sanaani Spoken Arabic,11350000,11350000,Afro-Asiatic
diff --git a/business_questions.sql b/business_questions.sql
new file mode 100644
index 0000000..a801343
--- /dev/null
+++ b/business_questions.sql
@@ -0,0 +1,70 @@
+SELECT * FROM top_languages_db.top_languages_db;
+
+USE top_languages_db;
+
+SHOW tables;
+
+SHOW COLUMNS FROM top_languages_db;
+
+DESCRIBE top_languages_db;
+ 
+ -- 1. What are the top 5 most spoken languages in the world by total speakers? 
+SELECT Language, "Total Speakers"
+FROM top_languages_db
+ORDER BY "Total Speakers" DESC
+LIMIT 5;
+
+-- 2. What are the top 5 languages with the highest number of native speakers?
+SELECT Language, "Native Speakers"
+FROM top_languages_db
+ORDER BY "Native Speakers" DESC
+LIMIT 5;
+
+-- 3. Which languages have the largest difference between native speakers and total speakers?
+SELECT Language, ("Total Speakers" -  "Native Speakers") AS Difference
+FROM top_languages_db
+ORDER BY Difference DESC
+LIMIT 5;
+
+-- 4. What is the proportion of native speakers compared to total speakers for each language?
+SELECT Language, ("Native Speakers" / "Total Speakers") * 100 AS Native_To_Total_Ratio
+FROM top_languages_db
+ORDER BY Native_To_Total_Ratio DESC;
+
+-- 5. Which language origins (language families) have the highest total number of speakers?
+SELECT Origin, SUM("Total Speakers") AS "Total Speakers"
+FROM top_languages_db
+GROUP BY Origin
+ORDER BY "Total Speakers" DESC;
+
+-- 6. How has the number of native and total speakers for the main languages evolved over time?
+SELECT Language, Year, "Total Speakers", "Native Speakers"
+FROM language_speakers_history
+WHERE Language IN ('English', 'Mandarin Chinese', 'Hindi', 'Spanish', 'French')
+ORDER BY Year ASC;
+
+-- 7. Which languages are the most spoken in specific continents (e.g., Europe)?
+SELECT Language, Continent, "Total Speakers"
+FROM top_languages_db
+WHERE Continent = 'Europe'
+ORDER BY "Total Speakers" DESC;
+
+-- 8. Which language has seen the greatest growth in total speakers in recent decades?
+SELECT Language, (MAX("Total Speakers") - MIN("Total Speakers")) AS Growth
+FROM language_speakers_history
+GROUP BY Language
+ORDER BY Growth DESC
+LIMIT 5;
+
+-- 9. Which languages have more speakers outside their country of origin?
+SELECT Language, ("Total Speakers" - "Native Speakers") AS Foreign_Speakers
+FROM top_languages_db
+WHERE Foreign_Speakers > "Native Speakers"
+ORDER BY Foreign_Speakers DESC;
+
+-- 10. Which countries contribute the most to the total number of speakers of a specific language? 
+SELECT Country, Language, SUM("Total Speakers") AS "Total Speakers"
+FROM language_by_country
+WHERE Language = 'English' -- Substitua pela língua desejada
+GROUP BY Country
+ORDER BY "Total Speakers" DESC;
\ No newline at end of file
diff --git a/main.ipynb b/main.ipynb
new file mode 100644
index 0000000..a39d108
--- /dev/null
+++ b/main.ipynb
@@ -0,0 +1,681 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Language</th>\n",
+       "      <th>Total Speakers</th>\n",
+       "      <th>Native Speakers</th>\n",
+       "      <th>Origin</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>English</td>\n",
+       "      <td>1132366680</td>\n",
+       "      <td>379007140.0</td>\n",
+       "      <td>Indo-European</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mandarin Chinese</td>\n",
+       "      <td>1116596640</td>\n",
+       "      <td>917868640.0</td>\n",
+       "      <td>Sino-Tibetan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Hindi</td>\n",
+       "      <td>615475540</td>\n",
+       "      <td>341208640.0</td>\n",
+       "      <td>Indo-European</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Spanish</td>\n",
+       "      <td>534335730</td>\n",
+       "      <td>460093030.0</td>\n",
+       "      <td>Indo-European</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>French</td>\n",
+       "      <td>279821930</td>\n",
+       "      <td>77177210.0</td>\n",
+       "      <td>Indo-European</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>Cameroonian Pidgin</td>\n",
+       "      <td>12000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Indo-European</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>Sylheti</td>\n",
+       "      <td>11800000</td>\n",
+       "      <td>10300000.0</td>\n",
+       "      <td>Indo-European</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>South Levantine Spoken Arabic</td>\n",
+       "      <td>11601100</td>\n",
+       "      <td>11601100.0</td>\n",
+       "      <td>Afro-Asiatic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>Tunisian Spoken Arabic</td>\n",
+       "      <td>11571600</td>\n",
+       "      <td>11571600.0</td>\n",
+       "      <td>Afro-Asiatic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>Sanaani Spoken Arabic</td>\n",
+       "      <td>11350000</td>\n",
+       "      <td>11350000.0</td>\n",
+       "      <td>Afro-Asiatic</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         Language  Total Speakers  Native Speakers  \\\n",
+       "0                         English      1132366680      379007140.0   \n",
+       "1                Mandarin Chinese      1116596640      917868640.0   \n",
+       "2                           Hindi       615475540      341208640.0   \n",
+       "3                         Spanish       534335730      460093030.0   \n",
+       "4                          French       279821930       77177210.0   \n",
+       "..                            ...             ...              ...   \n",
+       "95             Cameroonian Pidgin        12000000              NaN   \n",
+       "96                        Sylheti        11800000       10300000.0   \n",
+       "97  South Levantine Spoken Arabic        11601100       11601100.0   \n",
+       "98         Tunisian Spoken Arabic        11571600       11571600.0   \n",
+       "99          Sanaani Spoken Arabic        11350000       11350000.0   \n",
+       "\n",
+       "           Origin  \n",
+       "0   Indo-European  \n",
+       "1    Sino-Tibetan  \n",
+       "2   Indo-European  \n",
+       "3   Indo-European  \n",
+       "4   Indo-European  \n",
+       "..            ...  \n",
+       "95  Indo-European  \n",
+       "96  Indo-European  \n",
+       "97   Afro-Asiatic  \n",
+       "98   Afro-Asiatic  \n",
+       "99   Afro-Asiatic  \n",
+       "\n",
+       "[100 rows x 4 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 100 entries, 0 to 99\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column           Non-Null Count  Dtype  \n",
+      "---  ------           --------------  -----  \n",
+      " 0   Language         100 non-null    object \n",
+      " 1   Total Speakers   100 non-null    int64  \n",
+      " 2   Native Speakers  96 non-null     float64\n",
+      " 3   Origin           100 non-null    object \n",
+      "dtypes: float64(1), int64(1), object(2)\n",
+      "memory usage: 3.3+ KB\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "None"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>Total Speakers</th>\n",
+       "      <th>Native Speakers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1.000000e+02</td>\n",
+       "      <td>9.600000e+01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>8.276973e+07</td>\n",
+       "      <td>5.838473e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.770947e+08</td>\n",
+       "      <td>1.153839e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.135000e+07</td>\n",
+       "      <td>2.208000e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.524678e+07</td>\n",
+       "      <td>1.451462e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2.888955e+07</td>\n",
+       "      <td>2.460148e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>6.543730e+07</td>\n",
+       "      <td>5.368886e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.132367e+09</td>\n",
+       "      <td>9.178686e+08</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Total Speakers  Native Speakers\n",
+       "count    1.000000e+02     9.600000e+01\n",
+       "mean     8.276973e+07     5.838473e+07\n",
+       "std      1.770947e+08     1.153839e+08\n",
+       "min      1.135000e+07     2.208000e+06\n",
+       "25%      1.524678e+07     1.451462e+07\n",
+       "50%      2.888955e+07     2.460148e+07\n",
+       "75%      6.543730e+07     5.368886e+07\n",
+       "max      1.132367e+09     9.178686e+08"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "from sqlalchemy import create_engine\n",
+    "from sqlalchemy import text\n",
+    "\n",
+    "df = pd.read_csv('Top 100 Languages.csv')\n",
+    "\n",
+    "display(df)\n",
+    "\n",
+    "#display(df.head())\n",
+    "\n",
+    "display(df.info())\n",
+    "\n",
+    "display(df.describe())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            Language       Total Speakers      Native Speakers               Origin\n",
+      "             English           1132366680            379007140        Indo-European\n",
+      "    Mandarin Chinese           1116596640            917868640         Sino-Tibetan\n",
+      "               Hindi            615475540            341208640        Indo-European\n",
+      "             Spanish            534335730            460093030        Indo-European\n",
+      "              French            279821930             77177210        Indo-European\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = {\n",
+    "    'Language': ['English', 'Mandarin Chinese', 'Hindi', 'Spanish', 'French'],\n",
+    "    'Total Speakers': [1132366680, 1116596640, 615475540, 534335730, 279821930],\n",
+    "    'Native Speakers': [379007140, 917868640, 341208640, 460093030, 77177210],\n",
+    "    'Origin': ['Indo-European', 'Sino-Tibetan', 'Indo-European', 'Indo-European', 'Indo-European']\n",
+    "}\n",
+    "\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "print(df.to_string(index=False, col_space=20))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Language           0\n",
+      "Total Speakers     0\n",
+      "Native Speakers    0\n",
+      "Origin             0\n",
+      "dtype: int64\n",
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.isnull().sum())\n",
+    "\n",
+    "print(df.duplicated().sum())\n",
+    "\n",
+    "df = df.drop_duplicates()\n",
+    "\n",
+    "df = df.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Language', 'Total Speakers', 'Native Speakers', 'Origin'], dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.columns = df.columns.str.replace(' ', '_')\n",
+    "\n",
+    "df_sorted = df.sort_values(by='Total_Speakers', ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_sorted = df.sort_values(by='Total_Speakers', ascending=False)\n",
+    "\n",
+    "plt.figure(figsize=(10,6))\n",
+    "sns.barplot(x='Language', y='Total_Speakers', data=df_sorted.head(10))\n",
+    "plt.title('Top 5 Most Spoken Languages in the World')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()\n",
+    "\n",
+    "df_sorted = df.sort_values(by='Total_Speakers', ascending=False)\n",
+    "\n",
+    "plt.figure(figsize=(10,6))\n",
+    "\n",
+    "x = np.arange(len(df_sorted['Language'].head(10)))\n",
+    "width = 0.4\n",
+    "\n",
+    "plt.bar(x - width/2, df_sorted['Native_Speakers'].head(10), width, label='Native Speakers', color='blue', alpha=0.6)\n",
+    "plt.bar(x + width/2, df_sorted['Total_Speakers'].head(10), width, label='Total Speakers', color='orange', alpha=0.6)\n",
+    "\n",
+    "plt.title('Comparison of Native and Total Speakers - Top 5 Languages')\n",
+    "plt.xlabel('Language')\n",
+    "plt.ylabel('Number of Speakers')\n",
+    "plt.xticks(ticks=x, labels=df_sorted['Language'].head(10), rotation=45)\n",
+    "\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                 Native_Speakers  Total_Speakers\n",
+      "Native_Speakers         1.000000        0.720257\n",
+      "Total_Speakers          0.720257        1.000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "correlation = df[['Native_Speakers', 'Total_Speakers']].corr()\n",
+    "print(correlation)\n",
+    "\n",
+    "sns.heatmap(correlation, annot=True)\n",
+    "plt.title('Correlation between Native and Total Speakers')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Connection open\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pymysql\n",
+    "\n",
+    "cnx = pymysql.connect(user='root', password=input(\"Give me your password\"),\n",
+    "                      host='localhost')\n",
+    "if cnx.open:\n",
+    "    print(\"Connection open\")\n",
+    "else:\n",
+    "    print(\"Connection is not successfully open\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cursor = cnx.cursor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('bank',)\n",
+      "('information_schema',)\n",
+      "('languages',)\n",
+      "('mysql',)\n",
+      "('performance_schema',)\n",
+      "('publications',)\n",
+      "('sakila',)\n",
+      "('sys',)\n",
+      "('test_database',)\n",
+      "('top_languages_db',)\n"
+     ]
+    }
+   ],
+   "source": [
+    "cursor.execute(\"SHOW DATABASES\")\n",
+    "for db in cursor:\n",
+    "    print(db)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"USE top_languages_db\"\n",
+    "cursor.execute(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"\"\"CREATE TABLE IF NOT EXISTS top_languages_db (\n",
+    "        Language VARCHAR(255) PRIMARY KEY,\n",
+    "        Total_Speakers INT,\n",
+    "        Native_Speakers INT,\n",
+    "        Origin VARCHAR(255)\n",
+    "        )\"\"\"\n",
+    "\n",
+    "cursor.execute(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cnx.commit()\n",
+    "\n",
+    "cursor.close()\n",
+    "\n",
+    "cnx.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#pip install sqlalchemy\n",
+    "#pip install pymysql  # Para MySQL o MariaDB"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "engine = create_engine('mysql+pymysql://root:Adrian021218@localhost/top_languages_db')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame({\n",
+    "    'Language': ['English', 'Mandarin Chinese', 'Hindi', 'Spanish', 'French'],\n",
+    "    'Total Speakers': [1132366680, 1116596640, 615475540, 534335730, 279821930],\n",
+    "    'Native Speakers': [379007140, 917868640, 341208640, 460093030, 77177210],\n",
+    "    'Origin': ['Indo-European', 'Sino-Tibetan', 'Indo-European', 'Indo-European', 'Indo-European']\n",
+    "})\n",
+    "\n",
+    "df.to_sql('top_languages_db', con=engine, if_exists='replace', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('English', 1132366680, 379007140, 'Indo-European')\n",
+      "('Mandarin Chinese', 1116596640, 917868640, 'Sino-Tibetan')\n",
+      "('Hindi', 615475540, 341208640, 'Indo-European')\n",
+      "('Spanish', 534335730, 460093030, 'Indo-European')\n",
+      "('French', 279821930, 77177210, 'Indo-European')\n"
+     ]
+    }
+   ],
+   "source": [
+    "with engine.connect() as connection:\n",
+    "    result = connection.execute(text(\"SELECT * FROM top_languages\"))\n",
+    "    for row in result:\n",
+    "        print(row)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df.to_csv('top_languages.csv', index=False)"
+   ]
+  }
+ ],
+ "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": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/main.py b/main.py
new file mode 100644
index 0000000..ebdd4f2
--- /dev/null
+++ b/main.py
@@ -0,0 +1,111 @@
+# IMPORTING LIBRARIES
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+from sqlalchemy import create_engine
+from sqlalchemy import text
+
+
+# READING THE DATA
+
+df = pd.read_csv('Top 100 Languages.csv')
+
+
+# EXPLORATORY DATA ANALYSIS (EDA)
+
+print(df.head())
+print(df.info())
+print(df.describe())
+
+
+# CLEANING THE DATA
+
+print(df.isnull().sum())
+print(df.duplicated().sum())
+
+df = df.drop_duplicates()
+df = df.dropna()
+
+
+# RENAMING COLUMNS FOR CONSISTENCY (REMOVE SPACES)
+
+print(df.columns)
+df.columns = df.columns.str.replace(' ', '_')
+
+
+# SORTING THE DATA
+
+df_sorted = df.sort_values(by='Total_Speakers', ascending=False)
+
+
+# PLOTTING THE TOP 10 LANGUAGES BY TOTAL SPEAKERS
+
+plt.figure(figsize=(10,6))
+sns.barplot(x='Language', y='Total_Speakers', data=df_sorted.head(10))
+plt.title('Top 5 Most Spoken Languages in the World')
+plt.xticks(rotation=45)
+plt.show()
+
+
+# COMPARISON BETWEEN NATIVE AND TOTAL SPEAKERS FOR TOP 10 LANGUAGES
+
+df_sorted = df.sort_values(by='Total_Speakers', ascending=False)
+
+plt.figure(figsize=(10,6))
+x = np.arange(len(df_sorted['Language'].head(10)))
+width = 0.4
+plt.bar(x - width/2, df_sorted['Native_Speakers'].head(10), width, label='Native Speakers', color='blue', alpha=0.6)
+plt.bar(x + width/2, df_sorted['Total_Speakers'].head(10), width, label='Total Speakers', color='orange', alpha=0.6)
+plt.title('Comparison of Native and Total Speakers - Top 5 Languages')
+plt.xlabel('Language')
+plt.ylabel('Number of Speakers')
+plt.xticks(ticks=x, labels=df_sorted['Language'].head(10), rotation=45)
+plt.legend()
+plt.show()
+
+
+# CORRELATION ANALYSIS BETWEEN NATIVE AND TOTAL SPEAKERS
+
+correlation = df[['Native_Speakers', 'Total_Speakers']].corr()
+print(correlation)
+
+
+# HEATMAP OF CORRELATION MATRIX
+
+sns.heatmap(correlation, annot=True)
+plt.title('Correlation between Native and Total Speakers')
+plt.show()
+
+
+# CREATING THE ENGINE TO CONNECT TO THE DATABASE
+
+engine = create_engine('mysql+pymysql://root:Adrian021218@localhost/top_languages_db')
+
+
+# CREATING A NEW DATAFRAME FOR DATABASE INSERTION
+
+df = pd.DataFrame({
+    'Language': ['English', 'Mandarin Chinese', 'Hindi', 'Spanish', 'French'],
+    'Total Speakers': [1132366680, 1116596640, 615475540, 534335730, 279821930],
+    'Native Speakers': [379007140, 917868640, 341208640, 460093030, 77177210],
+    'Origin': ['Indo-European', 'Sino-Tibetan', 'Indo-European', 'Indo-European', 'Indo-European']
+})
+
+
+# UPLOADING THE DATA TO THE MYSQL DATABASE
+
+df.to_sql('top_languages_db', con=engine, if_exists='replace', index=False)
+
+
+# EXECUTING A QUERY TO RETRIEVE DATA
+
+with engine.connect() as connection:
+    result = connection.execute(text("SELECT * FROM top_languages"))
+    for row in result:
+        print(row)
+
+
+# EXPORTING DATA BACK TO CSV (if needed)
+# df.to_csv('top_languages_db.csv', index=False)
\ No newline at end of file
diff --git a/notes.pdf b/notes.pdf
new file mode 100644
index 0000000..09c9dd2
Binary files /dev/null and b/notes.pdf differ