diff --git a/Ironhack Payment EDA.pdf b/Ironhack Payment EDA.pdf
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diff --git a/Ironhack Payments - Project 1.twb b/Ironhack Payments - Project 1.twb
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+++ b/Ironhack Payments - Project 1.twb	
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+<?xml version='1.0' encoding='utf-8' ?>
+
+<!-- build 20242.24.0807.0327                               -->
+<workbook original-version='18.1' source-build='2024.2.2 (20242.24.0807.0327)' source-platform='win' version='18.1' xml:base='https://public.tableau.com' xmlns:user='http://www.tableausoftware.com/xml/user'>
+  <document-format-change-manifest>
+    <_.fcp.AccessibleZoneTabOrder.true...AccessibleZoneTabOrder />
+    <_.fcp.AnimationOnByDefault.true...AnimationOnByDefault />
+    <AutoCreateAndUpdateDSDPhoneLayouts />
+    <_.fcp.MarkAnimation.true...MarkAnimation />
+    <_.fcp.ObjectModelEncapsulateLegacy.true...ObjectModelEncapsulateLegacy />
+    <_.fcp.ObjectModelExtractV2.true...ObjectModelExtractV2 />
+    <_.fcp.ObjectModelTableType.true...ObjectModelTableType />
+    <_.fcp.SchemaViewerObjectModel.true...SchemaViewerObjectModel />
+    <SetMembershipControl />
+    <SheetIdentifierTracking />
+    <SortTagCleanup />
+    <_.fcp.VConnDownstreamExtractsWithWarnings.true...VConnDownstreamExtractsWithWarnings />
+    <WindowsPersistSimpleIdentifiers />
+  </document-format-change-manifest>
+  <repository-location id='IronHackPaymentProject_17268419095340' path='/workbooks' revision='1.0' />
+  <preferences>
+    <preference name='ui.encoding.shelf.height' value='24' />
+    <preference name='ui.shelf.height' value='26' />
+  </preferences>
+  <_.fcp.AnimationOnByDefault.false...style>
+    <_.fcp.AnimationOnByDefault.false..._.fcp.MarkAnimation.true...style-rule element='animation'>
+      <_.fcp.AnimationOnByDefault.false...format attr='animation-on' value='ao-on' />
+    </_.fcp.AnimationOnByDefault.false..._.fcp.MarkAnimation.true...style-rule>
+  </_.fcp.AnimationOnByDefault.false...style>
+  <datasources>
+    <datasource caption='frequency_of_usage+' inline='true' name='federated.0gap6dt0o22ay613fan5l0jigjn1' version='18.1'>
+      <connection class='federated'>
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+          <named-connection caption='frequency_of_usage' name='textscan.1bizpk41v7w8w2173h7000zxf07v'>
+            <connection class='textscan' directory='C:/Users/falabella/GIT/project-1-ironhack-payments-2-en/tableau' filename='frequency_of_usage.csv' password='' server='' workgroup-auth-mode='as-is' />
+          </named-connection>
+          <named-connection caption='incident_rate' name='textscan.15pk0qg0dsd5ym1bbgbbb1pygrac'>
+            <connection class='textscan' directory='C:/Users/falabella/GIT/project-1-ironhack-payments-2-en/tableau' filename='incident_rate.csv' password='' server='' workgroup-auth-mode='as-is' />
+          </named-connection>
+          <named-connection caption='loss_revenue' name='textscan.1ixahnp0rtys5419vxpw21ohnoxe'>
+            <connection class='textscan' directory='C:/Users/falabella/GIT/project-1-ironhack-payments-2-en/tableau' filename='loss_revenue.csv' password='' server='' workgroup-auth-mode='as-is' />
+          </named-connection>
+          <named-connection caption='revenue_per_month' name='textscan.0dni3n41p8qwn611omby501usu5u'>
+            <connection class='textscan' directory='C:/Users/falabella/GIT/project-1-ironhack-payments-2-en/tableau' filename='revenue_per_month.csv' password='' server='' workgroup-auth-mode='as-is' />
+          </named-connection>
+        </named-connections>
+        <_.fcp.ObjectModelEncapsulateLegacy.false...relation connection='textscan.1bizpk41v7w8w2173h7000zxf07v' name='frequency_of_usage.csv' table='[frequency_of_usage#csv]' type='table'>
+          <columns character-set='UTF-8' header='yes' locale='en_US' separator=','>
+            <column datatype='string' name='created_at' ordinal='0' />
+            <column datatype='integer' name='amount' ordinal='1' />
+          </columns>
+        </_.fcp.ObjectModelEncapsulateLegacy.false...relation>
+        <_.fcp.ObjectModelEncapsulateLegacy.true...relation type='collection'>
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+              <column datatype='string' name='created_at' ordinal='0' />
+              <column datatype='integer' name='amount' ordinal='1' />
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+              <column datatype='string' name='F1' ordinal='0' />
+              <column datatype='real' name='F2' ordinal='1' />
+            </columns>
+          </relation>
+          <relation connection='textscan.1bizpk41v7w8w2173h7000zxf07v' name='loss_revenue.csv' table='[loss_revenue#csv]' type='table'>
+            <columns character-set='UTF-8' header='yes' locale='en_US' separator=','>
+              <column datatype='string' name='created_at_y' ordinal='0' />
+              <column datatype='real' name='total_amount' ordinal='1' />
+            </columns>
+          </relation>
+          <relation connection='textscan.1bizpk41v7w8w2173h7000zxf07v' name='revenue_per_month.csv' table='[revenue_per_month#csv]' type='table'>
+            <columns character-set='UTF-8' header='yes' locale='en_US' separator=','>
+              <column datatype='string' name='created_at_y' ordinal='0' />
+              <column datatype='integer' name='total_amount' ordinal='1' />
+            </columns>
+          </relation>
+        </_.fcp.ObjectModelEncapsulateLegacy.true...relation>
+        <cols>
+          <map key='[F1]' value='[incident_rate.csv].[F1]' />
+          <map key='[F2]' value='[incident_rate.csv].[F2]' />
+          <map key='[amount]' value='[frequency_of_usage.csv].[amount]' />
+          <map key='[created_at]' value='[frequency_of_usage.csv].[created_at]' />
+          <map key='[created_at_y (revenue_per_month.csv)]' value='[revenue_per_month.csv].[created_at_y]' />
+          <map key='[created_at_y]' value='[loss_revenue.csv].[created_at_y]' />
+          <map key='[total_amount (revenue_per_month.csv)]' value='[revenue_per_month.csv].[total_amount]' />
+          <map key='[total_amount]' value='[loss_revenue.csv].[total_amount]' />
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+            <parent-name>[frequency_of_usage.csv]</parent-name>
+            <remote-alias />
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+              <attribute datatype='string' name='collation'>&quot;en_US&quot;</attribute>
+              <attribute datatype='string' name='field-delimiter'>&quot;,&quot;</attribute>
+              <attribute datatype='string' name='header-row'>&quot;true&quot;</attribute>
+              <attribute datatype='string' name='locale'>&quot;en_US&quot;</attribute>
+              <attribute datatype='string' name='single-char'>&quot;&quot;</attribute>
+            </attributes>
+          </metadata-record>
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+            <parent-name>[incident_rate.csv]</parent-name>
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+            <aggregation>Count</aggregation>
+            <contains-null>true</contains-null>
+            <attributes>
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+              <attribute datatype='string' name='collation'>&quot;en_US&quot;</attribute>
+              <attribute datatype='string' name='field-delimiter'>&quot;,&quot;</attribute>
+              <attribute datatype='string' name='header-row'>&quot;false&quot;</attribute>
+              <attribute datatype='string' name='locale'>&quot;en_US&quot;</attribute>
+              <attribute datatype='string' name='single-char'>&quot;&quot;</attribute>
+            </attributes>
+          </metadata-record>
+          <metadata-record class='capability'>
+            <remote-name />
+            <remote-type>0</remote-type>
+            <parent-name>[loss_revenue.csv]</parent-name>
+            <remote-alias />
+            <aggregation>Count</aggregation>
+            <contains-null>true</contains-null>
+            <attributes>
+              <attribute datatype='string' name='character-set'>&quot;UTF-8&quot;</attribute>
+              <attribute datatype='string' name='collation'>&quot;en_US&quot;</attribute>
+              <attribute datatype='string' name='field-delimiter'>&quot;,&quot;</attribute>
+              <attribute datatype='string' name='header-row'>&quot;true&quot;</attribute>
+              <attribute datatype='string' name='locale'>&quot;en_US&quot;</attribute>
+              <attribute datatype='string' name='single-char'>&quot;&quot;</attribute>
+            </attributes>
+          </metadata-record>
+          <metadata-record class='capability'>
+            <remote-name />
+            <remote-type>0</remote-type>
+            <parent-name>[revenue_per_month.csv]</parent-name>
+            <remote-alias />
+            <aggregation>Count</aggregation>
+            <contains-null>true</contains-null>
+            <attributes>
+              <attribute datatype='string' name='character-set'>&quot;UTF-8&quot;</attribute>
+              <attribute datatype='string' name='collation'>&quot;en_US&quot;</attribute>
+              <attribute datatype='string' name='field-delimiter'>&quot;,&quot;</attribute>
+              <attribute datatype='string' name='header-row'>&quot;true&quot;</attribute>
+              <attribute datatype='string' name='locale'>&quot;en_US&quot;</attribute>
+              <attribute datatype='string' name='single-char'>&quot;&quot;</attribute>
+            </attributes>
+          </metadata-record>
+          <metadata-record class='column'>
+            <remote-name>created_at</remote-name>
+            <remote-type>129</remote-type>
+            <local-name>[created_at]</local-name>
+            <parent-name>[frequency_of_usage.csv]</parent-name>
+            <remote-alias>created_at</remote-alias>
+            <ordinal>0</ordinal>
+            <local-type>string</local-type>
+            <aggregation>Count</aggregation>
+            <scale>1</scale>
+            <width>1073741823</width>
+            <contains-null>true</contains-null>
+            <collation flag='0' name='LEN_RUS' />
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+          </metadata-record>
+          <metadata-record class='column'>
+            <remote-name>amount</remote-name>
+            <remote-type>20</remote-type>
+            <local-name>[amount]</local-name>
+            <parent-name>[frequency_of_usage.csv]</parent-name>
+            <remote-alias>amount</remote-alias>
+            <ordinal>1</ordinal>
+            <local-type>integer</local-type>
+            <aggregation>Sum</aggregation>
+            <contains-null>true</contains-null>
+            <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[frequency_of_usage.csv_FAC41C66C96D422BA9B41BF6D8A22966]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
+          </metadata-record>
+          <metadata-record class='column'>
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+            <remote-type>129</remote-type>
+            <local-name>[F1]</local-name>
+            <parent-name>[incident_rate.csv]</parent-name>
+            <remote-alias>F1</remote-alias>
+            <ordinal>2</ordinal>
+            <local-type>string</local-type>
+            <aggregation>Count</aggregation>
+            <scale>1</scale>
+            <width>1073741823</width>
+            <contains-null>true</contains-null>
+            <collation flag='0' name='LEN_RUS' />
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+          </metadata-record>
+          <metadata-record class='column'>
+            <remote-name>F2</remote-name>
+            <remote-type>5</remote-type>
+            <local-name>[F2]</local-name>
+            <parent-name>[incident_rate.csv]</parent-name>
+            <remote-alias>F2</remote-alias>
+            <ordinal>3</ordinal>
+            <local-type>real</local-type>
+            <aggregation>Sum</aggregation>
+            <contains-null>true</contains-null>
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+          </metadata-record>
+          <metadata-record class='column'>
+            <remote-name>created_at_y</remote-name>
+            <remote-type>129</remote-type>
+            <local-name>[created_at_y]</local-name>
+            <parent-name>[loss_revenue.csv]</parent-name>
+            <remote-alias>created_at_y</remote-alias>
+            <ordinal>4</ordinal>
+            <local-type>string</local-type>
+            <aggregation>Count</aggregation>
+            <scale>1</scale>
+            <width>1073741823</width>
+            <contains-null>true</contains-null>
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+          </metadata-record>
+          <metadata-record class='column'>
+            <remote-name>total_amount</remote-name>
+            <remote-type>5</remote-type>
+            <local-name>[total_amount]</local-name>
+            <parent-name>[loss_revenue.csv]</parent-name>
+            <remote-alias>total_amount</remote-alias>
+            <ordinal>5</ordinal>
+            <local-type>real</local-type>
+            <aggregation>Sum</aggregation>
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+          </metadata-record>
+          <metadata-record class='column'>
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+            <remote-type>129</remote-type>
+            <local-name>[created_at_y (revenue_per_month.csv)]</local-name>
+            <parent-name>[revenue_per_month.csv]</parent-name>
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+            <ordinal>6</ordinal>
+            <local-type>string</local-type>
+            <aggregation>Count</aggregation>
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+          </metadata-record>
+          <metadata-record class='column'>
+            <remote-name>total_amount</remote-name>
+            <remote-type>20</remote-type>
+            <local-name>[total_amount (revenue_per_month.csv)]</local-name>
+            <parent-name>[revenue_per_month.csv]</parent-name>
+            <remote-alias>total_amount</remote-alias>
+            <ordinal>7</ordinal>
+            <local-type>integer</local-type>
+            <aggregation>Sum</aggregation>
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+      <column datatype='string' name='[F1]' role='dimension' type='nominal'>
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+      <column caption='Amount' datatype='integer' name='[amount]' role='measure' type='quantitative' />
+      <column caption='Created At' datatype='string' name='[created_at]' role='dimension' type='nominal'>
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+          <alias key='&quot;2019-11&quot;' value='11-2019' />
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+          <alias key='&quot;2020-06&quot;' value='06-2020' />
+          <alias key='&quot;2020-07&quot;' value='07-2020' />
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+          <alias key='&quot;2020-11&quot;' value='11-2020' />
+        </aliases>
+      </column>
+      <column caption='Created At Y (Revenue Per Month.Csv)' datatype='string' name='[created_at_y (revenue_per_month.csv)]' role='dimension' type='nominal'>
+        <aliases>
+          <alias key='&quot;2020-05&quot;' value='05-2020' />
+          <alias key='&quot;2020-06&quot;' value='06-2020' />
+          <alias key='&quot;2020-07&quot;' value='07-2020' />
+          <alias key='&quot;2020-08&quot;' value='08-2020' />
+          <alias key='&quot;2020-09&quot;' value='09-2020' />
+          <alias key='&quot;2020-10&quot;' value='10-2020' />
+          <alias key='&quot;2020-11&quot;' value='11-2020' />
+        </aliases>
+      </column>
+      <column caption='Created At Y' datatype='string' name='[created_at_y]' role='dimension' type='nominal'>
+        <aliases>
+          <alias key='&quot;2020-04&quot;' value='04-2020' />
+          <alias key='&quot;2020-05&quot;' value='05-2020' />
+          <alias key='&quot;2020-06&quot;' value='06-2020' />
+          <alias key='&quot;2020-07&quot;' value='07-2020' />
+          <alias key='&quot;2020-08&quot;' value='08-2020' />
+          <alias key='&quot;2020-09&quot;' value='09-2020' />
+          <alias key='&quot;2020-10&quot;' value='10-2020' />
+          <alias key='&quot;2020-11&quot;' value='11-2020' />
+        </aliases>
+      </column>
+      <column caption='Total Amount (Revenue Per Month.Csv)' datatype='integer' name='[total_amount (revenue_per_month.csv)]' role='measure' type='quantitative' />
+      <column caption='Total Amount' datatype='real' name='[total_amount]' role='measure' type='quantitative' />
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+          <cols>
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+            <map key='[created_at]' value='[frequency_of_usage.csv_FAC41C66C96D422BA9B41BF6D8A22966].[created_at]' />
+            <map key='[created_at_y (revenue_per_month.csv)]' value='[revenue_per_month.csv_5BBE77F856F446DEA4E1FEDE2028DC77].[created_at_y]' />
+            <map key='[created_at_y]' value='[loss_revenue.csv_86D572F8D9194E8DB3ABC93AD8AD5C22].[created_at_y]' />
+            <map key='[total_amount (revenue_per_month.csv)]' value='[revenue_per_month.csv_5BBE77F856F446DEA4E1FEDE2028DC77].[total_amount]' />
+            <map key='[total_amount]' value='[loss_revenue.csv_86D572F8D9194E8DB3ABC93AD8AD5C22].[total_amount]' />
+          </cols>
+          <metadata-records>
+            <metadata-record class='column'>
+              <remote-name>created_at</remote-name>
+              <remote-type>129</remote-type>
+              <local-name>[created_at]</local-name>
+              <parent-name>[frequency_of_usage.csv_FAC41C66C96D422BA9B41BF6D8A22966]</parent-name>
+              <remote-alias>created_at</remote-alias>
+              <ordinal>0</ordinal>
+              <family>frequency_of_usage.csv</family>
+              <local-type>string</local-type>
+              <aggregation>Count</aggregation>
+              <approx-count>13</approx-count>
+              <contains-null>true</contains-null>
+              <collation flag='0' name='LEN_RUS' />
+              <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[frequency_of_usage.csv_FAC41C66C96D422BA9B41BF6D8A22966]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
+            </metadata-record>
+            <metadata-record class='column'>
+              <remote-name>amount</remote-name>
+              <remote-type>20</remote-type>
+              <local-name>[amount]</local-name>
+              <parent-name>[frequency_of_usage.csv_FAC41C66C96D422BA9B41BF6D8A22966]</parent-name>
+              <remote-alias>amount</remote-alias>
+              <ordinal>1</ordinal>
+              <family>frequency_of_usage.csv</family>
+              <local-type>integer</local-type>
+              <aggregation>Sum</aggregation>
+              <approx-count>13</approx-count>
+              <contains-null>true</contains-null>
+              <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[frequency_of_usage.csv_FAC41C66C96D422BA9B41BF6D8A22966]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
+            </metadata-record>
+            <metadata-record class='column'>
+              <remote-name>F1</remote-name>
+              <remote-type>129</remote-type>
+              <local-name>[F1]</local-name>
+              <parent-name>[incident_rate.csv_293B15D130604F669F3B7849576DBA94]</parent-name>
+              <remote-alias>F1</remote-alias>
+              <ordinal>2</ordinal>
+              <family>incident_rate.csv</family>
+              <local-type>string</local-type>
+              <aggregation>Count</aggregation>
+              <approx-count>14</approx-count>
+              <contains-null>true</contains-null>
+              <collation flag='0' name='LEN_RUS' />
+              <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[incident_rate.csv_293B15D130604F669F3B7849576DBA94]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
+            </metadata-record>
+            <metadata-record class='column'>
+              <remote-name>F2</remote-name>
+              <remote-type>5</remote-type>
+              <local-name>[F2]</local-name>
+              <parent-name>[incident_rate.csv_293B15D130604F669F3B7849576DBA94]</parent-name>
+              <remote-alias>F2</remote-alias>
+              <ordinal>3</ordinal>
+              <family>incident_rate.csv</family>
+              <local-type>real</local-type>
+              <aggregation>Sum</aggregation>
+              <approx-count>8</approx-count>
+              <contains-null>true</contains-null>
+              <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[incident_rate.csv_293B15D130604F669F3B7849576DBA94]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
+            </metadata-record>
+            <metadata-record class='column'>
+              <remote-name>created_at_y</remote-name>
+              <remote-type>129</remote-type>
+              <local-name>[created_at_y]</local-name>
+              <parent-name>[loss_revenue.csv_86D572F8D9194E8DB3ABC93AD8AD5C22]</parent-name>
+              <remote-alias>created_at_y</remote-alias>
+              <ordinal>4</ordinal>
+              <family>loss_revenue.csv</family>
+              <local-type>string</local-type>
+              <aggregation>Count</aggregation>
+              <approx-count>8</approx-count>
+              <contains-null>true</contains-null>
+              <collation flag='0' name='LEN_RUS' />
+              <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[loss_revenue.csv_86D572F8D9194E8DB3ABC93AD8AD5C22]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
+            </metadata-record>
+            <metadata-record class='column'>
+              <remote-name>total_amount</remote-name>
+              <remote-type>5</remote-type>
+              <local-name>[total_amount]</local-name>
+              <parent-name>[loss_revenue.csv_86D572F8D9194E8DB3ABC93AD8AD5C22]</parent-name>
+              <remote-alias>total_amount</remote-alias>
+              <ordinal>5</ordinal>
+              <family>loss_revenue.csv</family>
+              <local-type>real</local-type>
+              <aggregation>Sum</aggregation>
+              <approx-count>8</approx-count>
+              <contains-null>true</contains-null>
+              <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[loss_revenue.csv_86D572F8D9194E8DB3ABC93AD8AD5C22]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
+            </metadata-record>
+            <metadata-record class='column'>
+              <remote-name>created_at_y</remote-name>
+              <remote-type>129</remote-type>
+              <local-name>[created_at_y (revenue_per_month.csv)]</local-name>
+              <parent-name>[revenue_per_month.csv_5BBE77F856F446DEA4E1FEDE2028DC77]</parent-name>
+              <remote-alias>created_at_y</remote-alias>
+              <ordinal>6</ordinal>
+              <family>revenue_per_month.csv</family>
+              <local-type>string</local-type>
+              <aggregation>Count</aggregation>
+              <approx-count>7</approx-count>
+              <contains-null>true</contains-null>
+              <collation flag='0' name='LEN_RUS' />
+              <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[revenue_per_month.csv_5BBE77F856F446DEA4E1FEDE2028DC77]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
+            </metadata-record>
+            <metadata-record class='column'>
+              <remote-name>total_amount</remote-name>
+              <remote-type>20</remote-type>
+              <local-name>[total_amount (revenue_per_month.csv)]</local-name>
+              <parent-name>[revenue_per_month.csv_5BBE77F856F446DEA4E1FEDE2028DC77]</parent-name>
+              <remote-alias>total_amount</remote-alias>
+              <ordinal>7</ordinal>
+              <family>revenue_per_month.csv</family>
+              <local-type>integer</local-type>
+              <aggregation>Sum</aggregation>
+              <approx-count>7</approx-count>
+              <contains-null>true</contains-null>
+              <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[revenue_per_month.csv_5BBE77F856F446DEA4E1FEDE2028DC77]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
+            </metadata-record>
+          </metadata-records>
+        </connection>
+      </extract>
+      <layout _.fcp.SchemaViewerObjectModel.false...dim-percentage='0.5' _.fcp.SchemaViewerObjectModel.false...measure-percentage='0.4' dim-ordering='alphabetic' measure-ordering='alphabetic' show-structure='true' />
+      <semantic-values>
+        <semantic-value key='[Country].[Name]' value='&quot;United States&quot;' />
+      </semantic-values>
+      <_.fcp.ObjectModelEncapsulateLegacy.true...object-graph>
+        <objects>
+          <object caption='frequency_of_usage.csv' id='frequency_of_usage.csv_FAC41C66C96D422BA9B41BF6D8A22966'>
+            <properties context=''>
+              <relation connection='textscan.1bizpk41v7w8w2173h7000zxf07v' name='frequency_of_usage.csv' table='[frequency_of_usage#csv]' type='table'>
+                <columns character-set='UTF-8' header='yes' locale='en_US' separator=','>
+                  <column datatype='string' name='created_at' ordinal='0' />
+                  <column datatype='integer' name='amount' ordinal='1' />
+                </columns>
+              </relation>
+            </properties>
+            <properties context='extract'>
+              <relation name='frequency_of_usage.csv_FAC41C66C96D422BA9B41BF6D8A22966' table='[Extract].[frequency_of_usage.csv_FAC41C66C96D422BA9B41BF6D8A22966]' type='table' />
+            </properties>
+          </object>
+          <object caption='incident_rate.csv' id='incident_rate.csv_293B15D130604F669F3B7849576DBA94'>
+            <properties context=''>
+              <relation connection='textscan.1bizpk41v7w8w2173h7000zxf07v' name='incident_rate.csv' table='[incident_rate#csv]' type='table'>
+                <columns character-set='UTF-8' header='no' locale='en_US' separator=','>
+                  <column datatype='string' name='F1' ordinal='0' />
+                  <column datatype='real' name='F2' ordinal='1' />
+                </columns>
+              </relation>
+            </properties>
+            <properties context='extract'>
+              <relation name='incident_rate.csv_293B15D130604F669F3B7849576DBA94' table='[Extract].[incident_rate.csv_293B15D130604F669F3B7849576DBA94]' type='table' />
+            </properties>
+          </object>
+          <object caption='loss_revenue.csv' id='loss_revenue.csv_86D572F8D9194E8DB3ABC93AD8AD5C22'>
+            <properties context=''>
+              <relation connection='textscan.1bizpk41v7w8w2173h7000zxf07v' name='loss_revenue.csv' table='[loss_revenue#csv]' type='table'>
+                <columns character-set='UTF-8' header='yes' locale='en_US' separator=','>
+                  <column datatype='string' name='created_at_y' ordinal='0' />
+                  <column datatype='real' name='total_amount' ordinal='1' />
+                </columns>
+              </relation>
+            </properties>
+            <properties context='extract'>
+              <relation name='loss_revenue.csv_86D572F8D9194E8DB3ABC93AD8AD5C22' table='[Extract].[loss_revenue.csv_86D572F8D9194E8DB3ABC93AD8AD5C22]' type='table' />
+            </properties>
+          </object>
+          <object caption='revenue_per_month.csv' id='revenue_per_month.csv_5BBE77F856F446DEA4E1FEDE2028DC77'>
+            <properties context=''>
+              <relation connection='textscan.1bizpk41v7w8w2173h7000zxf07v' name='revenue_per_month.csv' table='[revenue_per_month#csv]' type='table'>
+                <columns character-set='UTF-8' header='yes' locale='en_US' separator=','>
+                  <column datatype='string' name='created_at_y' ordinal='0' />
+                  <column datatype='integer' name='total_amount' ordinal='1' />
+                </columns>
+              </relation>
+            </properties>
+            <properties context='extract'>
+              <relation name='revenue_per_month.csv_5BBE77F856F446DEA4E1FEDE2028DC77' table='[Extract].[revenue_per_month.csv_5BBE77F856F446DEA4E1FEDE2028DC77]' type='table' />
+            </properties>
+          </object>
+        </objects>
+        <relationships>
+          <relationship>
+            <expression op='='>
+              <expression op='[created_at]' />
+              <expression op='[F1]' />
+            </expression>
+            <first-end-point object-id='frequency_of_usage.csv_FAC41C66C96D422BA9B41BF6D8A22966' />
+            <second-end-point object-id='incident_rate.csv_293B15D130604F669F3B7849576DBA94' />
+          </relationship>
+          <relationship>
+            <expression op='='>
+              <expression op='[F1]' />
+              <expression op='[created_at_y]' />
+            </expression>
+            <first-end-point object-id='incident_rate.csv_293B15D130604F669F3B7849576DBA94' />
+            <second-end-point object-id='loss_revenue.csv_86D572F8D9194E8DB3ABC93AD8AD5C22' />
+          </relationship>
+          <relationship>
+            <expression op='='>
+              <expression op='[F1]' />
+              <expression op='[created_at_y (revenue_per_month.csv)]' />
+            </expression>
+            <first-end-point object-id='incident_rate.csv_293B15D130604F669F3B7849576DBA94' />
+            <second-end-point object-id='revenue_per_month.csv_5BBE77F856F446DEA4E1FEDE2028DC77' />
+          </relationship>
+        </relationships>
+      </_.fcp.ObjectModelEncapsulateLegacy.true...object-graph>
+    </datasource>
+  </datasources>
+  <worksheets>
+    <worksheet name='Frequency of Service Usage'>
+      <layout-options>
+        <title>
+          <formatted-text>
+            <run bold='true' fontalignment='1' fontcolor='#4e79a7'>&lt;</run>
+            <run bold='true' fontalignment='1' fontcolor='#4e79a7'>Sheet Name</run>
+            <run bold='true' fontalignment='1' fontcolor='#4e79a7'>&gt;</run>
+          </formatted-text>
+        </title>
+      </layout-options>
+      <table>
+        <view>
+          <datasources>
+            <datasource caption='frequency_of_usage+' name='federated.0gap6dt0o22ay613fan5l0jigjn1' />
+          </datasources>
+          <datasource-dependencies datasource='federated.0gap6dt0o22ay613fan5l0jigjn1'>
+            <column caption='Amount' datatype='integer' name='[amount]' role='measure' type='quantitative' />
+            <column caption='Created At' datatype='string' name='[created_at]' role='dimension' type='nominal'>
+              <aliases>
+                <alias key='&quot;2019-11&quot;' value='11-2019' />
+                <alias key='&quot;2019-12&quot;' value='12-2019' />
+                <alias key='&quot;2020-01&quot;' value='01-2020' />
+                <alias key='&quot;2020-02&quot;' value='02-2020' />
+                <alias key='&quot;2020-03&quot;' value='03-2020' />
+                <alias key='&quot;2020-04&quot;' value='04-2020' />
+                <alias key='&quot;2020-05&quot;' value='05-2020' />
+                <alias key='&quot;2020-06&quot;' value='06-2020' />
+                <alias key='&quot;2020-07&quot;' value='07-2020' />
+                <alias key='&quot;2020-08&quot;' value='08-2020' />
+                <alias key='&quot;2020-09&quot;' value='09-2020' />
+                <alias key='&quot;2020-10&quot;' value='10-2020' />
+                <alias key='&quot;2020-11&quot;' value='11-2020' />
+              </aliases>
+            </column>
+            <column-instance column='[created_at]' derivation='None' name='[none:created_at:nk]' pivot='key' type='nominal' />
+            <column-instance column='[amount]' derivation='Sum' name='[sum:amount:qk]' pivot='key' type='quantitative' />
+          </datasource-dependencies>
+          <manual-sort column='[federated.0gap6dt0o22ay613fan5l0jigjn1].[none:created_at:nk]' direction='ASC'>
+            <dictionary>
+              <bucket>&quot;2019-11&quot;</bucket>
+              <bucket>&quot;2019-12&quot;</bucket>
+              <bucket>&quot;2020-01&quot;</bucket>
+              <bucket>&quot;2020-02&quot;</bucket>
+              <bucket>&quot;2020-03&quot;</bucket>
+              <bucket>&quot;2020-04&quot;</bucket>
+              <bucket>&quot;2020-05&quot;</bucket>
+              <bucket>&quot;2020-06&quot;</bucket>
+              <bucket>&quot;2020-07&quot;</bucket>
+              <bucket>&quot;2020-08&quot;</bucket>
+              <bucket>&quot;2020-09&quot;</bucket>
+              <bucket>&quot;2020-10&quot;</bucket>
+              <bucket>&quot;2020-11&quot;</bucket>
+            </dictionary>
+          </manual-sort>
+          <aggregation value='true' />
+        </view>
+        <style>
+          <style-rule element='axis'>
+            <format attr='title' class='0' field='[federated.0gap6dt0o22ay613fan5l0jigjn1].[sum:amount:qk]' scope='rows' value='Number of Transactions' />
+          </style-rule>
+          <style-rule element='label'>
+            <format attr='text-format' field='[federated.0gap6dt0o22ay613fan5l0jigjn1].[sum:amount:qk]' value='n#,##0;-#,##0' />
+            <format attr='text-orientation' field='[federated.0gap6dt0o22ay613fan5l0jigjn1].[none:created_at:nk]' value='0' />
+          </style-rule>
+          <style-rule element='worksheet'>
+            <format attr='display-field-labels' scope='cols' value='false' />
+          </style-rule>
+          <style-rule element='axis-title'>
+            <format attr='font-weight' field='[federated.0gap6dt0o22ay613fan5l0jigjn1].[sum:amount:qk]' value='bold' />
+            <format attr='color' field='[federated.0gap6dt0o22ay613fan5l0jigjn1].[sum:amount:qk]' value='#4e79a7' />
+          </style-rule>
+        </style>
+        <panes>
+          <pane selection-relaxation-option='selection-relaxation-allow'>
+            <view>
+              <breakdown value='auto' />
+            </view>
+            <mark class='Automatic' />
+            <customized-tooltip>
+              <formatted-text>
+                <run fontcolor='#787878'>Month - Year:&#9;</run>
+                <run bold='true'><![CDATA[<[federated.0gap6dt0o22ay613fan5l0jigjn1].[none:created_at:nk]>]]></run>
+                <run>Æ&#10;</run>
+                <run fontcolor='#787878'>Number of Transactions:&#9;</run>
+                <run bold='true'><![CDATA[<[federated.0gap6dt0o22ay613fan5l0jigjn1].[sum:amount:qk]>]]></run>
+              </formatted-text>
+            </customized-tooltip>
+          </pane>
+        </panes>
+        <rows>[federated.0gap6dt0o22ay613fan5l0jigjn1].[sum:amount:qk]</rows>
+        <cols>[federated.0gap6dt0o22ay613fan5l0jigjn1].[none:created_at:nk]</cols>
+      </table>
+      <simple-id uuid='{371F1EEF-C5C1-4383-80B0-D31D2307FED8}' />
+    </worksheet>
+    <worksheet name='Incident Rate'>
+      <layout-options>
+        <title>
+          <formatted-text>
+            <run bold='true' fontalignment='1' fontcolor='#4e79a7'>&lt;</run>
+            <run bold='true' fontalignment='1' fontcolor='#4e79a7'>Sheet Name</run>
+            <run bold='true' fontalignment='1' fontcolor='#4e79a7'>&gt;</run>
+          </formatted-text>
+        </title>
+      </layout-options>
+      <table>
+        <view>
+          <datasources>
+            <datasource caption='frequency_of_usage+' name='federated.0gap6dt0o22ay613fan5l0jigjn1' />
+          </datasources>
+          <datasource-dependencies datasource='federated.0gap6dt0o22ay613fan5l0jigjn1'>
+            <column datatype='string' name='[F1]' role='dimension' type='nominal'>
+              <aliases>
+                <alias key='&quot;2019-11&quot;' value='11-2019' />
+                <alias key='&quot;2019-12&quot;' value='12-2019' />
+                <alias key='&quot;2020-01&quot;' value='01-2020' />
+                <alias key='&quot;2020-02&quot;' value='02-2020' />
+                <alias key='&quot;2020-03&quot;' value='03-2020' />
+                <alias key='&quot;2020-04&quot;' value='04-2020' />
+                <alias key='&quot;2020-05&quot;' value='05-2020' />
+                <alias key='&quot;2020-06&quot;' value='06-2020' />
+                <alias key='&quot;2020-07&quot;' value='07-2020' />
+                <alias key='&quot;2020-08&quot;' value='08-2020' />
+                <alias key='&quot;2020-09&quot;' value='09-2020' />
+                <alias key='&quot;2020-10&quot;' value='10-2020' />
+                <alias key='&quot;2020-11&quot;' value='11-2020' />
+              </aliases>
+            </column>
+            <column datatype='real' name='[F2]' role='measure' type='quantitative' />
+            <column-instance column='[F1]' derivation='None' name='[none:F1:nk]' pivot='key' type='nominal' />
+            <column-instance column='[F2]' derivation='Sum' name='[sum:F2:qk]' pivot='key' type='quantitative' />
+          </datasource-dependencies>
+          <filter class='categorical' column='[federated.0gap6dt0o22ay613fan5l0jigjn1].[none:F1:nk]'>
+            <groupfilter function='except' user:ui-domain='relevant' user:ui-enumeration='exclusive' user:ui-marker='enumerate'>
+              <groupfilter function='level-members' level='[none:F1:nk]' />
+              <groupfilter function='member' level='[none:F1:nk]' member='%null%' />
+            </groupfilter>
+          </filter>
+          <manual-sort column='[federated.0gap6dt0o22ay613fan5l0jigjn1].[none:F1:nk]' direction='ASC'>
+            <dictionary>
+              <bucket>&quot;2019-11&quot;</bucket>
+              <bucket>&quot;2019-12&quot;</bucket>
+              <bucket>&quot;2020-01&quot;</bucket>
+              <bucket>&quot;2020-02&quot;</bucket>
+              <bucket>&quot;2020-03&quot;</bucket>
+              <bucket>&quot;2020-04&quot;</bucket>
+              <bucket>&quot;2020-05&quot;</bucket>
+              <bucket>&quot;2020-06&quot;</bucket>
+              <bucket>&quot;2020-07&quot;</bucket>
+              <bucket>&quot;2020-08&quot;</bucket>
+              <bucket>&quot;2020-09&quot;</bucket>
+              <bucket>&quot;2020-10&quot;</bucket>
+              <bucket>&quot;2020-11&quot;</bucket>
+            </dictionary>
+          </manual-sort>
+          <slices>
+            <column>[federated.0gap6dt0o22ay613fan5l0jigjn1].[none:F1:nk]</column>
+          </slices>
+          <aggregation value='true' />
+        </view>
+        <style>
+          <style-rule element='axis'>
+            <format attr='title' class='0' field='[federated.0gap6dt0o22ay613fan5l0jigjn1].[sum:F2:qk]' scope='rows' value='Percentage' />
+          </style-rule>
+          <style-rule element='label'>
+            <format attr='text-orientation' field='[federated.0gap6dt0o22ay613fan5l0jigjn1].[none:F1:nk]' value='0' />
+          </style-rule>
+          <style-rule element='table'>
+            <format attr='show-null-value-warning' value='false' />
+          </style-rule>
+          <style-rule element='worksheet'>
+            <format attr='display-field-labels' scope='cols' value='false' />
+          </style-rule>
+          <style-rule element='axis-title'>
+            <format attr='color' field='[federated.0gap6dt0o22ay613fan5l0jigjn1].[sum:F2:qk]' value='#4e79a7' />
+            <format attr='font-weight' field='[federated.0gap6dt0o22ay613fan5l0jigjn1].[sum:F2:qk]' value='bold' />
+          </style-rule>
+        </style>
+        <panes>
+          <pane selection-relaxation-option='selection-relaxation-allow'>
+            <view>
+              <breakdown value='auto' />
+            </view>
+            <mark class='Automatic' />
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+    <thumbnail height='192' name='Incident Rate' width='192'>
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+    </thumbnail>
+    <thumbnail height='192' name='Loss Revenue' width='192'>
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+    </thumbnail>
+    <thumbnail height='192' name='Revenue per Month' width='192'>
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+</workbook>
diff --git a/main.ipynb b/main.ipynb
new file mode 100644
index 0000000..d21fa3b
--- /dev/null
+++ b/main.ipynb
@@ -0,0 +1,521 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import Pandas, Numpy and Matplot\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Exploratory Data Analysis (EDA) of Cash Request \n",
+    "\n",
+    "\"\"\"Before diving into cohort analysis, conduct an exploratory data analysis to gain a \n",
+    "comprehensive understanding of the dataset. Explore key statistics, distributions, \n",
+    "and visualizations to identify patterns and outliers. EDA will help you make informed \n",
+    "decisions on data preprocessing and analysis strategies.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# STEP 1: Read the document and Create a Dataframe\n",
+    "# Document type: CSV\n",
+    "df_cash = pd.read_csv(\"project_dataset\\extract - cash request - data analyst.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# STEP 2: Start the Exploratory Analysis\n",
+    "\n",
+    "# a: Shape of the DataFrame (rows, columns)\n",
+    "data_shape = df_cash.shape\n",
+    "print(\"Shape of the DataFrame:\")\n",
+    "print(data_shape)\n",
+    "\n",
+    "# b: Column Names\n",
+    "column_names = df_cash.columns\n",
+    "print(\"Column Names:\")\n",
+    "print(column_names)\n",
+    "\n",
+    "# c: Data types of each column\n",
+    "data_types = df_cash.dtypes\n",
+    "print(\"Data Types:\")\n",
+    "print(data_types)\n",
+    "\n",
+    "# d: General Information about the DataFrame\n",
+    "data_info = df_cash.info()\n",
+    "print(\"General Information about the DataFrame:\")\n",
+    "print(data_info)\n",
+    "\n",
+    "# e: Statistical Summary\n",
+    "data_summary = df_cash.describe()\n",
+    "print(\"\\nStatistical Summary:\")\n",
+    "print(data_summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Data Quality Analysis of Cash Request\n",
+    "\n",
+    "\"\"\"Assess the quality of the dataset by identifying missing values, \n",
+    "data inconsistencies, and potential errors. \n",
+    "Implement data cleaning and preprocessing steps to ensure the reliability of your analysis.\n",
+    "Document any data quality issues encountered and the steps taken to address them.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# STEP 3: Start the Data Quality Analysis\n",
+    "\n",
+    "# a: Check for missing values\n",
+    "# Check for missing values in the DataFrame and clean the dataFrame\n",
+    "missing_values = pd.isnull(df_cash)\n",
+    "df_cash_cleaned = df_cash.drop_duplicates().fillna(0)\n",
+    "# Count missing values in each column\n",
+    "missing_counts = missing_values.sum()\n",
+    "\n",
+    "# Count columns with missing values\n",
+    "columns_with_missing = missing_counts[missing_counts > 0].count()\n",
+    "\n",
+    "# Check if all columns have missing values\n",
+    "all_columns_missing = missing_counts.all()\n",
+    "\n",
+    "# Calculate the total number of missing values\n",
+    "total_missing_values = missing_counts.sum()\n",
+    "\n",
+    "# Display the results\n",
+    "print(\"Missing Values in Each Column:\\n\", missing_counts)\n",
+    "print(\"\\nNumber of Columns with Missing Values:\", columns_with_missing)\n",
+    "print(\"All Columns Have Missing Values:\", all_columns_missing)\n",
+    "print(\"\\nTotal Missing Values in the DataFrame:\", total_missing_values)\n",
+    "print(df_cash_cleaned.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Exploratory Data Analysis (EDA) of Fees\n",
+    "\n",
+    "\"\"\"Before diving into cohort analysis, conduct an exploratory data analysis to gain a \n",
+    "comprehensive understanding of the dataset. Explore key statistics, distributions, \n",
+    "and visualizations to identify patterns and outliers. EDA will help you make informed \n",
+    "decisions on data preprocessing and analysis strategies.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# STEP 1: Read the document and Create a Dataframe\n",
+    "# Document type: CSV\n",
+    "df_fees = pd.read_csv(\"project_dataset/extract - fees - data analyst - .csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# STEP 2: Start the Exploratory Analysis\n",
+    "\n",
+    "# a: Shape of the DataFrame (rows, columns)\n",
+    "data_shape = df_fees.shape\n",
+    "print(\"Shape of the DataFrame:\")\n",
+    "print(data_shape)\n",
+    "\n",
+    "# b: Column Names\n",
+    "column_names = df_fees.columns\n",
+    "print(\"Column Names:\")\n",
+    "print(column_names)\n",
+    "\n",
+    "# c: Data types of each column\n",
+    "data_types = df_fees.dtypes\n",
+    "print(\"Data Types:\")\n",
+    "print(data_types)\n",
+    "\n",
+    "# d: General Information about the DataFrame\n",
+    "data_info = df_fees.info()\n",
+    "print(\"General Information about the DataFrame:\")\n",
+    "print(data_info)\n",
+    "\n",
+    "# e: Statistical Summary\n",
+    "data_summary = df_fees.describe()\n",
+    "print(\"\\nStatistical Summary:\")\n",
+    "print(data_summary)\n",
+    "print(df_fees)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Data Quality Analysis of Fees\n",
+    "\n",
+    "\"\"\"Assess the quality of the dataset by identifying missing values, \n",
+    "data inconsistencies, and potential errors. \n",
+    "Implement data cleaning and preprocessing steps to ensure the reliability of your analysis.\n",
+    "Document any data quality issues encountered and the steps taken to address them.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# STEP 3: Start the Data Quality Analysis\n",
+    "\n",
+    "# a: Check for missing values\n",
+    "# Check for missing values in the DataFrame\n",
+    "missing_values = pd.isnull(df_fees)\n",
+    "\n",
+    "# Count missing values in each column\n",
+    "missing_counts = missing_values.sum()\n",
+    "df_fees_cleaned = df_fees.drop_duplicates().fillna(0)\n",
+    "\n",
+    "# Count columns with missing values\n",
+    "columns_with_missing = missing_counts[missing_counts > 0].count()\n",
+    "\n",
+    "# Check if all columns have missing values\n",
+    "all_columns_missing = missing_counts.all()\n",
+    "\n",
+    "# Calculate the total number of missing values\n",
+    "total_missing_values = missing_counts.sum()\n",
+    "\n",
+    "# Display the results\n",
+    "print(\"Missing Values in Each Column:\\n\", missing_counts)\n",
+    "print(\"\\nNumber of Columns with Missing Values:\", columns_with_missing)\n",
+    "print(\"All Columns Have Missing Values:\", all_columns_missing)\n",
+    "print(\"\\nTotal Missing Values in the DataFrame:\", total_missing_values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Exploratory Data Analysis (EDA) of Lexique \n",
+    "\n",
+    "\"\"\"Before diving into cohort analysis, conduct an exploratory data analysis to gain a \n",
+    "comprehensive understanding of the dataset. Explore key statistics, distributions, \n",
+    "and visualizations to identify patterns and outliers. EDA will help you make informed \n",
+    "decisions on data preprocessing and analysis strategies.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# STEP 1: Read the document and Create a Dataframe\n",
+    "# Document type: XLSX\n",
+    "df_lexique = pd.read_excel(\"project_dataset/Lexique - Data Analyst.xlsx\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# STEP 2: Start the Exploratory Analysis\n",
+    "\n",
+    "# a: Shape of the DataFrame (rows, columns)\n",
+    "data_shape = df_lexique.shape\n",
+    "print(\"Shape of the DataFrame:\")\n",
+    "print(data_shape)\n",
+    "\n",
+    "# b: Column Names\n",
+    "column_names = df_lexique.columns\n",
+    "print(\"Column Names:\")\n",
+    "print(column_names)\n",
+    "\n",
+    "# c: Data types of each column\n",
+    "data_types = df_lexique.dtypes\n",
+    "print(\"Data Types:\")\n",
+    "print(data_types)\n",
+    "\n",
+    "# d: General Information about the DataFrame\n",
+    "data_info = df_lexique.info()\n",
+    "print(\"General Information about the DataFrame:\")\n",
+    "print(data_info)\n",
+    "\n",
+    "# e: Statistical Summary\n",
+    "data_summary = df_lexique.describe()\n",
+    "print(\"\\nStatistical Summary:\")\n",
+    "print(data_summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Data Quality Analysis of Lexique\n",
+    "\n",
+    "\"\"\"Assess the quality of the dataset by identifying missing values, \n",
+    "data inconsistencies, and potential errors. \n",
+    "Implement data cleaning and preprocessing steps to ensure the reliability of your analysis.\n",
+    "Document any data quality issues encountered and the steps taken to address them.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# STEP 3: Start the Data Quality Analysis\n",
+    "\n",
+    "# a: Check for missing values\n",
+    "# Check for missing values in the DataFrame\n",
+    "missing_values = pd.isnull(df_lexique)\n",
+    "\n",
+    "# Count missing values in each column\n",
+    "missing_counts = missing_values.sum()\n",
+    "\n",
+    "# Count columns with missing values\n",
+    "columns_with_missing = missing_counts[missing_counts > 0].count()\n",
+    "\n",
+    "# Check if all columns have missing values\n",
+    "all_columns_missing = missing_counts.all()\n",
+    "\n",
+    "# Calculate the total number of missing values\n",
+    "total_missing_values = missing_counts.sum()\n",
+    "\n",
+    "# Display the results\n",
+    "print(\"Missing Values in Each Column:\\n\", missing_counts)\n",
+    "print(\"\\nNumber of Columns with Missing Values:\", columns_with_missing)\n",
+    "print(\"All Columns Have Missing Values:\", all_columns_missing)\n",
+    "print(\"\\nTotal Missing Values in the DataFrame:\", total_missing_values)\n",
+    "\n",
+    "print(df_lexique)\n",
+    "print(df_cash_cleaned['created_at'].min())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Metrics to Analyze\n",
+    "\n",
+    "\"\"\"You will calculate and analyze the following metrics for each cohort:\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# METRIC 1. \n",
+    "\"\"\"**Frequency of Service Usage:** \n",
+    "Understand how often users \n",
+    "from each cohort utilize IronHack Payments' cash advance services over time.\n",
+    "\n",
+    "For this metric we are going to build the cohorts based on \"USER_ID\" and \"created_at\" \n",
+    "\"\"\"\n",
+    "\n",
+    "# Number of unique users:\n",
+    "target_columns = [\"user_id\", \"amount\"]\n",
+    "columns_to_colapse = [\"user_id\"]\n",
+    "aggregations = ['count', 'sum', 'mean', 'max', 'min']\n",
+    "users = df_cash_cleaned[target_columns].groupby(by=columns_to_colapse).agg(aggregations)\n",
+    "\n",
+    "print(\"Total number of unique users:\")\n",
+    "print(users.count().unique()) # 10798 unique users\n",
+    "\n",
+    "# Create the cohorts based on \"created_at\" and \"user_id\". Important to group time data by MONTH \"M\"\n",
+    "df_cash_cleaned['created_at'] = pd.to_datetime(df_cash_cleaned['created_at'])\n",
+    "cohorts = df_cash_cleaned.groupby(df_cash_cleaned['created_at'].dt.to_period(\"M\"))['amount'].count()\n",
+    "\n",
+    "# Create the visualization chart using \"plt\" to see the number of transactions per month, over time\n",
+    "cohorts.plot.bar(figsize=(12, 6))\n",
+    "plt.title('Service user over time')\n",
+    "plt.xlabel('Month')\n",
+    "plt.ylabel('Transactions')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# METRIC 2.\n",
+    "\"\"\"**Incident Rate:** Determine the incident rate, specifically focusing on payment incidents, for each cohort. \n",
+    "Identify if there are variations in incident rates among different cohorts.\"\"\"\n",
+    "\n",
+    "# Sort the dataframe by type.\n",
+    "df_fees_sorted = df_fees_cleaned.sort_values(by='type', ascending=True)\n",
+    "\n",
+    "# Slice the DataFrame to keep only the rows which column 'type' = 'incident'\n",
+    "df_fees_incidents = df_fees_cleaned[df_fees_cleaned['type'] == \"incident\"] # DataFrame with all the incidents\n",
+    "\n",
+    "# Calculate the number of transactions by count of 'df_fees_incidents'\n",
+    "number_of_incidents = df_fees_incidents['type'].count() # Returns 2196 \n",
+    "\n",
+    "# Merge (inner) both \"Cash Request\" and \"Fees\" matching the 'cash_request_id' from 'df_fees_incidents with 'id' on 'df_cash'\n",
+    "merged_df = pd.merge(df_fees_incidents, df_cash_cleaned, left_on=\"cash_request_id\", right_on='id', how='inner')\n",
+    "\n",
+    "# Group the incidents based on the month the request was created at and count them\n",
+    "merged_df['created_at_y'] = pd.to_datetime(merged_df['created_at_y'])\n",
+    "number_of_incidents_per_month = merged_df.groupby(merged_df['created_at_y'].dt.to_period(\"M\"))['type'].count()\n",
+    "print(merged_df.head())\n",
+    "\n",
+    "# Display 'cohort', 'number_of_incidents_per_month\n",
+    "print(cohorts.head())\n",
+    "print(number_of_incidents_per_month.head())\n",
+    "print(merged_df['created_at_y'].min())\n",
+    "\n",
+    "# Calculate the incident rate per month (percentage of number of incidents over total transactions in the same cohort)\n",
+    "incident_rate = (number_of_incidents_per_month / cohorts)*100\n",
+    "print(incident_rate)\n",
+    "\n",
+    "# Display the incidents per month on a chart\n",
+    "incident_rate.plot.bar(figsize=(12, 6))\n",
+    "plt.title('Incident Rate Per Month')\n",
+    "plt.xlabel('Month')\n",
+    "plt.ylabel('Percentage(%)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# METRIC 3.\n",
+    "\"\"\"**Revenue Generated by the Cohort:** Calculate the total revenue generated by \n",
+    "each cohort over months to assess the financial impact of user behavior.\"\"\"\n",
+    "\n",
+    "# Filter the dataFrame 'df_fees' by 'status' = 'accepted'\n",
+    "df_fees_accepted = df_fees_cleaned[df_fees_cleaned['status'] == \"accepted\"]\n",
+    "merged_df = pd.merge(df_fees_accepted, df_cash_cleaned, left_on=\"cash_request_id\", right_on='id', how='inner')\n",
+    "print(merged_df.columns)\n",
+    "\n",
+    "# Groub 'total_amount' by month based on 'created_at'\n",
+    "merged_df['created_at_y'] = pd.to_datetime(merged_df['created_at_y'])\n",
+    "revenue_per_month = merged_df[['created_at_y', 'total_amount']].groupby(merged_df['created_at_y'].dt.to_period(\"M\"))['total_amount'].count()\n",
+    "print(revenue_per_month)\n",
+    "\n",
+    "# Display the revenue per month on a chart\n",
+    "revenue_per_month.plot.bar(figsize=(12, 6))\n",
+    "plt.title('Revenue Per Month')\n",
+    "plt.xlabel('Month')\n",
+    "plt.ylabel('Amount ($)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# METRIC 4.\n",
+    "\"\"\"**Loss of Revenue due to cancelled transaction**.\"\"\"\n",
+    "\n",
+    "# Filter the dataFrame 'df_fees' by 'status' = 'accepted'\n",
+    "\n",
+    "cancelled = df_fees_cleaned[df_fees_cleaned['status']=='cancelled'].reset_index()\n",
+    "merged_cancelled_and_cash = pd.merge(cancelled, df_cash_cleaned, left_on=\"cash_request_id\", right_on='id', how='inner')\n",
+    "\n",
+    "# Groub 'total_amount' by month based on 'created_at_y'\n",
+    "merged_cancelled_and_cash['created_at_y'] = pd.to_datetime(merged_cancelled_and_cash['created_at_y'])\n",
+    "cancelled_cohorts = merged_cancelled_and_cash[['created_at_y', 'total_amount']].groupby(merged_cancelled_and_cash['created_at_y'].dt.to_period(\"M\"))['total_amount'].sum()\n",
+    "print(cancelled_cohorts)\n",
+    "\n",
+    "# Display the loss of revenue per month on a chart\n",
+    "cancelled_cohorts.plot.bar(figsize=(12, 6))\n",
+    "plt.title('Loss Revenue')\n",
+    "plt.xlabel('Month')\n",
+    "plt.ylabel('Amount ($)')\n",
+    "plt.show()\n",
+    "\n",
+    "cohorts.plot.bar(figsize=(12, 6))\n",
+    "plt.title('Frequency of Service Usage over time')\n",
+    "plt.xlabel('Month')\n",
+    "plt.ylabel('Transactions')\n",
+    "plt.show()\n",
+    "incident_rate.plot.bar(figsize=(12, 6))\n",
+    "plt.title('Incident Rate Per Month')\n",
+    "plt.xlabel('Month')\n",
+    "plt.ylabel('Percentage(%)')\n",
+    "plt.show()\n",
+    "revenue_per_month.plot.bar(figsize=(12, 6))\n",
+    "plt.title('Revenue Per Month')\n",
+    "plt.xlabel('Month')\n",
+    "plt.ylabel('Amount ($)')\n",
+    "plt.show()\n",
+    "cancelled_cohorts.plot.bar(figsize=(12, 6))\n",
+    "plt.title('Loss Revenue')\n",
+    "plt.xlabel('Month')\n",
+    "plt.ylabel('Amount ($)')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/main.py b/main.py
new file mode 100644
index 0000000..fc8009e
--- /dev/null
+++ b/main.py
@@ -0,0 +1,347 @@
+# Import Pandas, Numpy and Matplot
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+### Exploratory Data Analysis (EDA) of Cash Request 
+
+"""Before diving into cohort analysis, conduct an exploratory data analysis to gain a 
+comprehensive understanding of the dataset. Explore key statistics, distributions, 
+and visualizations to identify patterns and outliers. EDA will help you make informed 
+decisions on data preprocessing and analysis strategies."""
+
+# STEP 1: Read the document and Create a Dataframe
+# Document type: CSV
+df_cash = pd.read_csv("project_dataset\extract - cash request - data analyst.csv")
+
+# STEP 2: Start the Exploratory Analysis
+
+# a: Shape of the DataFrame (rows, columns)
+data_shape = df_cash.shape
+print("Shape of the DataFrame:")
+print(data_shape)
+
+# b: Column Names
+column_names = df_cash.columns
+print("Column Names:")
+print(column_names)
+
+# c: Data types of each column
+data_types = df_cash.dtypes
+print("Data Types:")
+print(data_types)
+
+# d: General Information about the DataFrame
+data_info = df_cash.info()
+print("General Information about the DataFrame:")
+print(data_info)
+
+# e: Statistical Summary
+data_summary = df_cash.describe()
+print("\nStatistical Summary:")
+print(data_summary)
+
+### Data Quality Analysis of Cash Request
+
+"""Assess the quality of the dataset by identifying missing values, 
+data inconsistencies, and potential errors. 
+Implement data cleaning and preprocessing steps to ensure the reliability of your analysis.
+Document any data quality issues encountered and the steps taken to address them."""
+
+# STEP 3: Start the Data Quality Analysis
+
+# a: Check for missing values
+# Check for missing values in the DataFrame and clean the dataFrame
+missing_values = pd.isnull(df_cash)
+df_cash_cleaned = df_cash.drop_duplicates().fillna(0)
+# Count missing values in each column
+missing_counts = missing_values.sum()
+
+# Count columns with missing values
+columns_with_missing = missing_counts[missing_counts > 0].count()
+
+# Check if all columns have missing values
+all_columns_missing = missing_counts.all()
+
+# Calculate the total number of missing values
+total_missing_values = missing_counts.sum()
+
+# Display the results
+print("Missing Values in Each Column:\n", missing_counts)
+print("\nNumber of Columns with Missing Values:", columns_with_missing)
+print("All Columns Have Missing Values:", all_columns_missing)
+print("\nTotal Missing Values in the DataFrame:", total_missing_values)
+print(df_cash_cleaned.shape)
+
+### Exploratory Data Analysis (EDA) of Fees
+
+"""Before diving into cohort analysis, conduct an exploratory data analysis to gain a 
+comprehensive understanding of the dataset. Explore key statistics, distributions, 
+and visualizations to identify patterns and outliers. EDA will help you make informed 
+decisions on data preprocessing and analysis strategies."""
+
+# STEP 1: Read the document and Create a Dataframe
+# Document type: CSV
+df_fees = pd.read_csv("project_dataset/extract - fees - data analyst - .csv")
+
+# STEP 2: Start the Exploratory Analysis
+
+# a: Shape of the DataFrame (rows, columns)
+data_shape = df_fees.shape
+print("Shape of the DataFrame:")
+print(data_shape)
+
+# b: Column Names
+column_names = df_fees.columns
+print("Column Names:")
+print(column_names)
+
+# c: Data types of each column
+data_types = df_fees.dtypes
+print("Data Types:")
+print(data_types)
+
+# d: General Information about the DataFrame
+data_info = df_fees.info()
+print("General Information about the DataFrame:")
+print(data_info)
+
+# e: Statistical Summary
+data_summary = df_fees.describe()
+print("\nStatistical Summary:")
+print(data_summary)
+print(df_fees)
+
+### Data Quality Analysis of Fees
+
+"""Assess the quality of the dataset by identifying missing values, 
+data inconsistencies, and potential errors. 
+Implement data cleaning and preprocessing steps to ensure the reliability of your analysis.
+Document any data quality issues encountered and the steps taken to address them."""
+
+# STEP 3: Start the Data Quality Analysis
+
+# a: Check for missing values
+# Check for missing values in the DataFrame
+missing_values = pd.isnull(df_fees)
+
+# Count missing values in each column
+missing_counts = missing_values.sum()
+df_fees_cleaned = df_fees.drop_duplicates().fillna(0)
+
+# Count columns with missing values
+columns_with_missing = missing_counts[missing_counts > 0].count()
+
+# Check if all columns have missing values
+all_columns_missing = missing_counts.all()
+
+# Calculate the total number of missing values
+total_missing_values = missing_counts.sum()
+
+# Display the results
+print("Missing Values in Each Column:\n", missing_counts)
+print("\nNumber of Columns with Missing Values:", columns_with_missing)
+print("All Columns Have Missing Values:", all_columns_missing)
+print("\nTotal Missing Values in the DataFrame:", total_missing_values)
+
+### Exploratory Data Analysis (EDA) of Lexique 
+
+"""Before diving into cohort analysis, conduct an exploratory data analysis to gain a 
+comprehensive understanding of the dataset. Explore key statistics, distributions, 
+and visualizations to identify patterns and outliers. EDA will help you make informed 
+decisions on data preprocessing and analysis strategies."""
+
+# STEP 1: Read the document and Create a Dataframe
+# Document type: XLSX
+df_lexique = pd.read_excel("project_dataset/Lexique - Data Analyst.xlsx")
+
+# STEP 2: Start the Exploratory Analysis
+
+# a: Shape of the DataFrame (rows, columns)
+data_shape = df_lexique.shape
+print("Shape of the DataFrame:")
+print(data_shape)
+
+# b: Column Names
+column_names = df_lexique.columns
+print("Column Names:")
+print(column_names)
+
+# c: Data types of each column
+data_types = df_lexique.dtypes
+print("Data Types:")
+print(data_types)
+
+# d: General Information about the DataFrame
+data_info = df_lexique.info()
+print("General Information about the DataFrame:")
+print(data_info)
+
+# e: Statistical Summary
+data_summary = df_lexique.describe()
+print("\nStatistical Summary:")
+print(data_summary)
+
+### Data Quality Analysis of Lexique
+
+"""Assess the quality of the dataset by identifying missing values, 
+data inconsistencies, and potential errors. 
+Implement data cleaning and preprocessing steps to ensure the reliability of your analysis.
+Document any data quality issues encountered and the steps taken to address them."""
+
+# STEP 3: Start the Data Quality Analysis
+
+# a: Check for missing values
+# Check for missing values in the DataFrame
+missing_values = pd.isnull(df_lexique)
+
+# Count missing values in each column
+missing_counts = missing_values.sum()
+
+# Count columns with missing values
+columns_with_missing = missing_counts[missing_counts > 0].count()
+
+# Check if all columns have missing values
+all_columns_missing = missing_counts.all()
+
+# Calculate the total number of missing values
+total_missing_values = missing_counts.sum()
+
+# Display the results
+print("Missing Values in Each Column:\n", missing_counts)
+print("\nNumber of Columns with Missing Values:", columns_with_missing)
+print("All Columns Have Missing Values:", all_columns_missing)
+print("\nTotal Missing Values in the DataFrame:", total_missing_values)
+
+print(df_lexique)
+print(df_cash_cleaned['created_at'].min())
+
+### Metrics to Analyze
+
+"""You will calculate and analyze the following metrics for each cohort:"""
+
+# METRIC 1. 
+"""**Frequency of Service Usage:** 
+Understand how often users 
+from each cohort utilize IronHack Payments' cash advance services over time.
+
+For this metric we are going to build the cohorts based on "USER_ID" and "created_at" 
+"""
+
+# Number of unique users:
+target_columns = ["user_id", "amount"]
+columns_to_colapse = ["user_id"]
+aggregations = ['count', 'sum', 'mean', 'max', 'min']
+users = df_cash_cleaned[target_columns].groupby(by=columns_to_colapse).agg(aggregations)
+
+print("Total number of unique users:")
+print(users.count().unique()) # 10798 unique users
+
+# Create the cohorts based on "created_at" and "user_id". Important to group time data by MONTH "M"
+df_cash_cleaned['created_at'] = pd.to_datetime(df_cash_cleaned['created_at'])
+cohorts = df_cash_cleaned.groupby(df_cash_cleaned['created_at'].dt.to_period("M"))['amount'].count()
+
+# Create the visualization chart using "plt" to see the number of transactions per month, over time
+cohorts.plot.bar(figsize=(12, 6))
+plt.title('Service user over time')
+plt.xlabel('Month')
+plt.ylabel('Transactions')
+plt.show()
+
+# METRIC 2.
+"""**Incident Rate:** Determine the incident rate, specifically focusing on payment incidents, for each cohort. 
+Identify if there are variations in incident rates among different cohorts."""
+
+# Sort the dataframe by type.
+df_fees_sorted = df_fees_cleaned.sort_values(by='type', ascending=True)
+
+# Slice the DataFrame to keep only the rows which column 'type' = 'incident'
+df_fees_incidents = df_fees_cleaned[df_fees_cleaned['type'] == "incident"] # DataFrame with all the incidents
+
+# Calculate the number of transactions by count of 'df_fees_incidents'
+number_of_incidents = df_fees_incidents['type'].count() # Returns 2196 
+
+# Merge (inner) both "Cash Request" and "Fees" matching the 'cash_request_id' from 'df_fees_incidents with 'id' on 'df_cash'
+merged_df = pd.merge(df_fees_incidents, df_cash_cleaned, left_on="cash_request_id", right_on='id', how='inner')
+
+# Group the incidents based on the month the request was created at and count them
+merged_df['created_at_y'] = pd.to_datetime(merged_df['created_at_y'])
+number_of_incidents_per_month = merged_df.groupby(merged_df['created_at_y'].dt.to_period("M"))['type'].count()
+print(merged_df.head())
+# Display 'cohort', 'number_of_incidents_per_month
+print(cohorts.head())
+print(number_of_incidents_per_month.head())
+print(merged_df['created_at_y'].min())
+
+# Calculate the incident rate per month (percentage of number of incidents over total transactions in the same cohort)
+incident_rate = (number_of_incidents_per_month / cohorts)*100
+print(incident_rate)
+
+# Display the incidents per month on a chart
+incident_rate.plot.bar(figsize=(12, 6))
+plt.title('Incident Rate Per Month')
+plt.xlabel('Month')
+plt.ylabel('Percentage(%)')
+plt.show()
+
+# METRIC 3.
+"""**Revenue Generated by the Cohort:** Calculate the total revenue generated by 
+each cohort over months to assess the financial impact of user behavior."""
+
+# Filter the dataFrame 'df_fees' by 'status' = 'accepted'
+df_fees_accepted = df_fees_cleaned[df_fees_cleaned['status'] == "accepted"]
+merged_df = pd.merge(df_fees_accepted, df_cash_cleaned, left_on="cash_request_id", right_on='id', how='inner')
+print(merged_df.columns)
+
+# Groub 'total_amount' by month based on 'created_at'
+merged_df['created_at_y'] = pd.to_datetime(merged_df['created_at_y'])
+revenue_per_month = merged_df[['created_at_y', 'total_amount']].groupby(merged_df['created_at_y'].dt.to_period("M"))['total_amount'].count()
+print(revenue_per_month)
+
+# Display the revenue per month on a chart
+revenue_per_month.plot.bar(figsize=(12, 6))
+plt.title('Revenue Per Month')
+plt.xlabel('Month')
+plt.ylabel('Amount ($)')
+plt.show()
+
+# METRIC 4.
+"""**Loss of Revenue due to cancelled transaction**."""
+
+# Filter the dataFrame 'df_fees' by 'status' = 'accepted'
+
+cancelled = df_fees_cleaned[df_fees_cleaned['status']=='cancelled'].reset_index()
+merged_cancelled_and_cash = pd.merge(cancelled, df_cash_cleaned, left_on="cash_request_id", right_on='id', how='inner')
+
+# Groub 'total_amount' by month based on 'created_at_y'
+merged_cancelled_and_cash['created_at_y'] = pd.to_datetime(merged_cancelled_and_cash['created_at_y'])
+cancelled_cohorts = merged_cancelled_and_cash[['created_at_y', 'total_amount']].groupby(merged_cancelled_and_cash['created_at_y'].dt.to_period("M"))['total_amount'].sum()
+print(cancelled_cohorts)
+
+# Display the loss of revenue per month on a chart
+cancelled_cohorts.plot.bar(figsize=(12, 6))
+plt.title('Loss Revenue')
+plt.xlabel('Month')
+plt.ylabel('Amount ($)')
+plt.show()
+
+cohorts.plot.bar(figsize=(12, 6))
+plt.title('Frequency of Service Usage over time')
+plt.xlabel('Month')
+plt.ylabel('Transactions')
+plt.show()
+incident_rate.plot.bar(figsize=(12, 6))
+plt.title('Incident Rate Per Month')
+plt.xlabel('Month')
+plt.ylabel('Percentage(%)')
+plt.show()
+revenue_per_month.plot.bar(figsize=(12, 6))
+plt.title('Revenue Per Month')
+plt.xlabel('Month')
+plt.ylabel('Amount ($)')
+plt.show()
+cancelled_cohorts.plot.bar(figsize=(12, 6))
+plt.title('Loss Revenue')
+plt.xlabel('Month')
+plt.ylabel('Amount ($)')
+plt.show()
\ No newline at end of file
diff --git a/readme.md b/readme.md
index 8a78b0f..a85449e 100644
--- a/readme.md
+++ b/readme.md
@@ -47,3 +47,7 @@ Assess the quality of the dataset by identifying missing values, data inconsiste
 <!-- this version uses Tableau which consorci doesn't use -->
 
 <!-- https://github.com/ironhack-labs/project-1-ironhack-payments-2-en -->
+
+## Tableau Dashboard:
+
+https://public.tableau.com/views/IronHackPaymentProject_17268419095340/Dashboard1?:language=pt-BR&publish=yes&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link
diff --git a/tableau/frequency_of_usage.csv b/tableau/frequency_of_usage.csv
new file mode 100644
index 0000000..efcb20b
--- /dev/null
+++ b/tableau/frequency_of_usage.csv
@@ -0,0 +1,14 @@
+created_at,amount
+2019-11,1
+2019-12,289
+2020-01,223
+2020-02,184
+2020-03,244
+2020-04,473
+2020-05,837
+2020-06,2615
+2020-07,3601
+2020-08,3417
+2020-09,4221
+2020-10,7725
+2020-11,140
diff --git a/tableau/incident_rate.csv b/tableau/incident_rate.csv
new file mode 100644
index 0000000..971e86d
--- /dev/null
+++ b/tableau/incident_rate.csv
@@ -0,0 +1,14 @@
+,0
+2019-11,
+2019-12,
+2020-01,
+2020-02,
+2020-03,
+2020-04,
+2020-05,5.256869772998805
+2020-06,21.414913957934992
+2020-07,18.30047209108581
+2020-08,14.574187884108866
+2020-09,9.12106135986733
+2020-10,0.6472491909385114
+2020-11,
diff --git a/tableau/loss_revenue.csv b/tableau/loss_revenue.csv
new file mode 100644
index 0000000..fb19eed
--- /dev/null
+++ b/tableau/loss_revenue.csv
@@ -0,0 +1,9 @@
+created_at_y,total_amount
+2020-04,5.0
+2020-05,740.0
+2020-06,3470.0
+2020-07,4150.0
+2020-08,4850.0
+2020-09,4990.0
+2020-10,6455.0
+2020-11,10.0
diff --git a/tableau/revenue_per_month.csv b/tableau/revenue_per_month.csv
new file mode 100644
index 0000000..750510c
--- /dev/null
+++ b/tableau/revenue_per_month.csv
@@ -0,0 +1,8 @@
+created_at_y,total_amount
+2020-05,104
+2020-06,937
+2020-07,1033
+2020-08,2340
+2020-09,3357
+2020-10,6967
+2020-11,103