diff --git a/.gitignore b/.gitignore index a549e70..1d00f56 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,11 @@ __pycache__ *.pytest_cache +<<<<<<< HEAD +.venv/ + +======= .env/ +>>>>>>> origin/main # .env *.db *.idea diff --git a/.ls-lint.yml b/.ls-lint.yml index e4d31eb..372d2fd 100644 --- a/.ls-lint.yml +++ b/.ls-lint.yml @@ -4,6 +4,9 @@ ls: .md: snake_case | regex:[0-9A-Z\-]+ .txt: snake_case | kebab-case .yml: snake_case | kebab-case + .ipynb: snake_case + .csv: snake_case + .py: snake_case .: # Répertoires racine .*/: @@ -15,13 +18,28 @@ ignore: - .git - .github - .vscode - - venv - .ruff_cache - .pytest_cache - __pycache__ +<<<<<<< HEAD + - .venv + - **/site-packages/** + - 1_datasets + - data + - dist + - build + +rules: + some_rule: "true" # string entre guillemets +ignore: + - ".venv/" + +description: "Check code formatting: true/false" +======= - .ls-lint.yml - .markdownlint.yml rules: some_rule: "true" +>>>>>>> origin/main diff --git a/1_datasets/haiti_catalog_raw_data/1st dataset_draft.....xlsx b/1_datasets/haiti_catalog_raw_data/1st dataset_draft.....xlsx index 660d1c3..b55615a 100644 Binary files a/1_datasets/haiti_catalog_raw_data/1st dataset_draft.....xlsx and b/1_datasets/haiti_catalog_raw_data/1st dataset_draft.....xlsx differ diff --git a/2_data_preparation/Extract_data_haiti_and_caribbean.ipynb b/2_data_preparation/Extract_data_haiti_and_caribbean.ipynb index 02075ab..bc9d873 100644 --- a/2_data_preparation/Extract_data_haiti_and_caribbean.ipynb +++ b/2_data_preparation/Extract_data_haiti_and_caribbean.ipynb @@ -187,13 +187,13 @@ ], "source": [ "# Load dataset for inbound UN arrivals arrivals tourist in the world\n", - "df = pd.read_excel (\"UN_Tourism_inbound_arrivals_10_2025.xlsx\")\n", + "df = pd.read_excel(\"UN_Tourism_inbound_arrivals_10_2025.xlsx\")\n", "df.head()\n", "# Load dataset for inbound UN arrivals arrivals tourist by purpose in the world\n", "df_purpose = pd.read_excel(\"UN_Tourism_inbound_arrivals_by_purpose_10_2025.xlsx\")\n", "df_purpose.head()\n", "# Load dataset for inbound UN arrivals arrivals tourist by transport in the world\n", - "df_transport = pd.read_excel (\"UN_Tourism_inbound_arrivals_by_transport_10_2025.xlsx\")\n", + "df_transport = pd.read_excel(\"UN_Tourism_inbound_arrivals_by_transport_10_2025.xlsx\")\n", "df_transport.head()\n", "# Load dataset for inbound expenditure by tourists in the world\n", "df_expenditure = pd.read_excel(\"UN_Tourism_inbound_expenditure_10_2025.xlsx\")\n", @@ -386,7 +386,9 @@ "df_haiti_by_transport = df_transport[(df_transport[\"reporter_area_label\"] == \"Haiti\")]\n", "df_haiti_by_transport.head()\n", "# Load the inbound expenditure by tourists in the world dataset to extract data only for Haiti\n", - "df_haiti_by_expenditure = df_expenditure[(df_expenditure[\"reporter_area_label\"] == \"Haiti\")]\n", + "df_haiti_by_expenditure = df_expenditure[\n", + " (df_expenditure[\"reporter_area_label\"] == \"Haiti\")\n", + "]\n", "df_haiti_by_expenditure.head()" ] }, @@ -398,13 +400,19 @@ "outputs": [], "source": [ "# save the df_haiti in a csv file\n", - "df_haiti.to_csv (\"UN_tourism_inbound_Arrivals_haiti.csv\",index = False)\n", + "df_haiti.to_csv(\"UN_tourism_inbound_Arrivals_haiti.csv\", index=False)\n", "# save the df_haiti_by_purpose in a csv file\n", - "df_haiti_by_purpose.to_csv(\"UN_tourism_inbound_Arrivals_haiti_by_purpose.csv\", index= False)\n", + "df_haiti_by_purpose.to_csv(\n", + " \"UN_tourism_inbound_Arrivals_haiti_by_purpose.csv\", index=False\n", + ")\n", "# save the df_haiti_by_transport in a csv file\n", - "df_haiti_by_transport.to_csv(\"UN_tourism_inbound_Arrivals_haiti_by_transport.csv\", index= False)\n", + "df_haiti_by_transport.to_csv(\n", + " \"UN_tourism_inbound_Arrivals_haiti_by_transport.csv\", index=False\n", + ")\n", "# save the df_haiti_by_expenditure in a csv file\n", - "df_haiti_by_expenditure.to_csv(\"UN_tourism_inbound_Arrivals_haiti_by_expenditure.csv\", index= False)" + "df_haiti_by_expenditure.to_csv(\n", + " \"UN_tourism_inbound_Arrivals_haiti_by_expenditure.csv\", index=False\n", + ")" ] }, { @@ -612,10 +620,10 @@ ], "source": [ "# df[\"reporter_area_label\"].unique()\n", - "#df_haiti_by_purpose[\"year\"].unique()\n", - "#df_haiti_by_purpose.isnull().sum()\n", - "#df_haiti_by_transport.isnull().sum()\n", - "#df_haiti_by_expenditure.isnull().sum()\n", + "# df_haiti_by_purpose[\"year\"].unique()\n", + "# df_haiti_by_purpose.isnull().sum()\n", + "# df_haiti_by_transport.isnull().sum()\n", + "# df_haiti_by_expenditure.isnull().sum()\n", "df_haiti_by_expenditure[\"year\"].unique()" ] }, @@ -628,15 +636,37 @@ "source": [ "# create a array with the names the caribbean country to extract data for the caribbean region\n", "caribbean_countries = [\n", - " \"Antigua and Barbuda\", \"Bahamas\", \"Barbados\", \"Belize\",\n", - " \"Cuba\", \"Dominica\", \"Dominican Republic\", \"Grenada\",\n", - " \"Jamaica\", \"Saint Kitts and Nevis\",\"Saint Lucia\",\"Haiti\",\n", - " \"Saint Vincent and the Grenadines\", \"Trinidad and Tobago\",\n", - " \"Anguilla\", \"Bermuda\", \"British Virgin Islands\", \"Cayman Islands\",\n", - " \"Montserrat\", \"Turks and Caicos Islands\", \"Guadeloupe\", \"Martinique\",\n", - " \"Aruba\", \"Curaçao\", \"Sint Maarten (Dutch part)\", \"Bonaire\",\n", - " \"Saba\", \"St. Eustatius\", \"Puerto Rico\", \"United States Virgin Islands\"\n", - "]\n" + " \"Antigua and Barbuda\",\n", + " \"Bahamas\",\n", + " \"Barbados\",\n", + " \"Belize\",\n", + " \"Cuba\",\n", + " \"Dominica\",\n", + " \"Dominican Republic\",\n", + " \"Grenada\",\n", + " \"Jamaica\",\n", + " \"Saint Kitts and Nevis\",\n", + " \"Saint Lucia\",\n", + " \"Haiti\",\n", + " \"Saint Vincent and the Grenadines\",\n", + " \"Trinidad and Tobago\",\n", + " \"Anguilla\",\n", + " \"Bermuda\",\n", + " \"British Virgin Islands\",\n", + " \"Cayman Islands\",\n", + " \"Montserrat\",\n", + " \"Turks and Caicos Islands\",\n", + " \"Guadeloupe\",\n", + " \"Martinique\",\n", + " \"Aruba\",\n", + " \"Curaçao\",\n", + " \"Sint Maarten (Dutch part)\",\n", + " \"Bonaire\",\n", + " \"Saba\",\n", + " \"St. Eustatius\",\n", + " \"Puerto Rico\",\n", + " \"United States Virgin Islands\",\n", + "]" ] }, { @@ -646,7 +676,7 @@ "metadata": {}, "outputs": [], "source": [ - "# filter the dataset to keep data only for the caribbean region \n", + "# filter the dataset to keep data only for the caribbean region\n", "df_caribbean = df[df[\"reporter_area_label\"].isin(caribbean_countries)]" ] }, diff --git a/2_data_preparation/cleaned_data_haiti.ipynb b/2_data_preparation/cleaned_data_haiti.ipynb index 3c97e74..f9cca82 100644 --- a/2_data_preparation/cleaned_data_haiti.ipynb +++ b/2_data_preparation/cleaned_data_haiti.ipynb @@ -186,11 +186,11 @@ } ], "source": [ - "#------------------------------------------------------------------------------\n", + "# ------------------------------------------------------------------------------\n", "# Step 1\n", - "#------------------------------------------------------------------------------\n", + "# ------------------------------------------------------------------------------\n", "# Load the UN_tourism_inbound_Arrivals_haiti for cleaning\n", - "df= pd.read_csv(\"UN_tourism_inbound_Arrivals_haiti.csv\")\n", + "df = pd.read_csv(\"UN_tourism_inbound_Arrivals_haiti.csv\")\n", "df.head()" ] }, @@ -201,9 +201,20 @@ "metadata": {}, "outputs": [], "source": [ - "# Dropping empty and irrelevant columns to clean the dataset for analysis \n", - "df_arrivals_haiti = df.drop(columns=[\"indicator_code\", \"indicator_previous_code\", \"indicator_previous_code\",\"reporter_area_code\",\n", - " \"partner_area_code\", \"flag\", \"flag_label\",\"notes\",\"drop\"])" + "# Dropping empty and irrelevant columns to clean the dataset for analysis\n", + "df_arrivals_haiti = df.drop(\n", + " columns=[\n", + " \"indicator_code\",\n", + " \"indicator_previous_code\",\n", + " \"indicator_previous_code\",\n", + " \"reporter_area_code\",\n", + " \"partner_area_code\",\n", + " \"flag\",\n", + " \"flag_label\",\n", + " \"notes\",\n", + " \"drop\",\n", + " ]\n", + ")" ] }, { @@ -213,13 +224,15 @@ "metadata": {}, "outputs": [], "source": [ - "# Rename indicator label categories to standardize types of visitors \n", - "df_arrivals_haiti[\"indicator_label\"] = df_arrivals_haiti[\"indicator_label\"].replace({\n", - " \"inbound - trips - total - cruise ships - same-day visitors (excursionists)\": \"excursionists\",\n", - " \"inbound - trips - total - total - overnight visitors (tourists)\": \"tourists\",\n", - " \"inbound - trips - total - total - same-day visitors (excursionists)\": \"excursionists\",\n", - " \"inbound - trips - total - total - visitors\": \"visitors_total\"\n", - "})" + "# Rename indicator label categories to standardize types of visitors\n", + "df_arrivals_haiti[\"indicator_label\"] = df_arrivals_haiti[\"indicator_label\"].replace(\n", + " {\n", + " \"inbound - trips - total - cruise ships - same-day visitors (excursionists)\": \"excursionists\",\n", + " \"inbound - trips - total - total - overnight visitors (tourists)\": \"tourists\",\n", + " \"inbound - trips - total - total - same-day visitors (excursionists)\": \"excursionists\",\n", + " \"inbound - trips - total - total - visitors\": \"visitors_total\",\n", + " }\n", + ")" ] }, { @@ -230,13 +243,15 @@ "outputs": [], "source": [ "# rename columns for more clarity\n", - "df_arrivals_haiti = df_arrivals_haiti.rename(columns={\n", - " \"indicator_label\": \"type_of_visitors\",\n", - " \"reporter_area_label\": \"country_receiving\",\n", - " \"partner_area_label\": \"where_tourist_from\",\n", - " \"value\": \"number_of_tourist\",\n", - " \"unit\" : \"unit_thousand_trips\"\n", - "})\n" + "df_arrivals_haiti = df_arrivals_haiti.rename(\n", + " columns={\n", + " \"indicator_label\": \"type_of_visitors\",\n", + " \"reporter_area_label\": \"country_receiving\",\n", + " \"partner_area_label\": \"where_tourist_from\",\n", + " \"value\": \"number_of_tourist\",\n", + " \"unit\": \"unit_thousand_trips\",\n", + " }\n", + ")" ] }, { @@ -368,7 +383,7 @@ } ], "source": [ - "# see the shape of the dataset \n", + "# see the shape of the dataset\n", "df_arrivals_haiti.shape" ] }, @@ -380,7 +395,7 @@ "outputs": [], "source": [ "# save the cleaned dataset in a csv file\n", - "df_arrivals_haiti.to_csv(\"UN_tourism_inbound_Arrivals_haiti_cleaned.csv\",index=False)" + "df_arrivals_haiti.to_csv(\"UN_tourism_inbound_Arrivals_haiti_cleaned.csv\", index=False)" ] }, { @@ -559,9 +574,9 @@ } ], "source": [ - "#------------------------------------------------------------------------------------\n", + "# ------------------------------------------------------------------------------------\n", "# step 2\n", - "#------------------------------------------------------------------------------------\n", + "# ------------------------------------------------------------------------------------\n", "# Load the UN_tourism_inbound_Arrivals_haiti_by_purpose for cleaning\n", "df_purpose = pd.read_csv(\"UN_tourism_inbound_Arrivals_haiti_by_purpose.csv\")\n", "df_purpose.head()" @@ -574,9 +589,20 @@ "metadata": {}, "outputs": [], "source": [ - "# Dropping empty and irrelevant columns to clean the dataset for analysis \n", - "df_purpose = df.drop(columns=[\"indicator_code\", \"indicator_previous_code\", \"indicator_previous_code\",\"reporter_area_code\",\n", - " \"partner_area_code\", \"flag\", \"flag_label\",\"notes\",\"drop\"])" + "# Dropping empty and irrelevant columns to clean the dataset for analysis\n", + "df_purpose = df.drop(\n", + " columns=[\n", + " \"indicator_code\",\n", + " \"indicator_previous_code\",\n", + " \"indicator_previous_code\",\n", + " \"reporter_area_code\",\n", + " \"partner_area_code\",\n", + " \"flag\",\n", + " \"flag_label\",\n", + " \"notes\",\n", + " \"drop\",\n", + " ]\n", + ")" ] }, { @@ -612,10 +638,13 @@ "outputs": [], "source": [ "# Rename indicator label categories to standardize types of visitors\n", - "df_purpose[\"indicator_label\"] = df_purpose[\"indicator_label\"].replace({\n", - " \"inbound - trips - by purpose - business - overnight visitors (tourists)\": \"business\",\n", - " \"inbound - trips - by purpose - personal - overnight visitors (tourists)\": \"personal\",\n", - " \"inbound - trips - by purpose - total - overnight visitors (tourists)\": \"total\"})" + "df_purpose[\"indicator_label\"] = df_purpose[\"indicator_label\"].replace(\n", + " {\n", + " \"inbound - trips - by purpose - business - overnight visitors (tourists)\": \"business\",\n", + " \"inbound - trips - by purpose - personal - overnight visitors (tourists)\": \"personal\",\n", + " \"inbound - trips - by purpose - total - overnight visitors (tourists)\": \"total\",\n", + " }\n", + ")" ] }, { @@ -634,13 +663,15 @@ "outputs": [], "source": [ "# rename columns for more clarity\n", - "df_purpose = df_purpose.rename(columns={\n", - " \"indicator_label\": \"purpose_of_visites\",\n", - " \"reporter_area_label\": \"country_receiving\",\n", - " \"partner_area_label\": \"where_tourist_from\",\n", - " \"value\": \"number_of_tourist_by_purpose\",\n", - " \"unit\" : \"unit_thousans_trips\"\n", - "})\n" + "df_purpose = df_purpose.rename(\n", + " columns={\n", + " \"indicator_label\": \"purpose_of_visites\",\n", + " \"reporter_area_label\": \"country_receiving\",\n", + " \"partner_area_label\": \"where_tourist_from\",\n", + " \"value\": \"number_of_tourist_by_purpose\",\n", + " \"unit\": \"unit_thousans_trips\",\n", + " }\n", + ")" ] }, { @@ -802,8 +833,10 @@ "metadata": {}, "outputs": [], "source": [ - "# save the cleaned data \n", - "df_purpose.to_csv(\"UN_tourism_inbound_Arrivals_haiti_by_purpose_cleaned.csv\",index=False)" + "# save the cleaned data\n", + "df_purpose.to_csv(\n", + " \"UN_tourism_inbound_Arrivals_haiti_by_purpose_cleaned.csv\", index=False\n", + ")" ] }, { @@ -982,11 +1015,11 @@ } ], "source": [ - "#----------------------------------------------------------------------------------\n", - "#step 3\n", - "#----------------------------------------------------------------------------------\n", - " # Load the UN_tourism_inbound_Arrivals_haiti_by_expenditure for cleaning\n", - "df_expenditure= pd.read_csv(\"UN_tourism_inbound_Arrivals_haiti_by_expenditure.csv\")\n", + "# ----------------------------------------------------------------------------------\n", + "# step 3\n", + "# ----------------------------------------------------------------------------------\n", + "# Load the UN_tourism_inbound_Arrivals_haiti_by_expenditure for cleaning\n", + "df_expenditure = pd.read_csv(\"UN_tourism_inbound_Arrivals_haiti_by_expenditure.csv\")\n", "df_expenditure.head()" ] }, @@ -1019,9 +1052,20 @@ "metadata": {}, "outputs": [], "source": [ - "# Dropping empty and irrelevant columns to clean the dataset for analysis \n", - "df_expenditure = df_expenditure.drop(columns=[\"indicator_code\", \"indicator_previous_code\", \"indicator_previous_code\",\"reporter_area_code\",\n", - " \"partner_area_code\", \"flag\", \"flag_label\",\"notes\",\"drop\"])" + "# Dropping empty and irrelevant columns to clean the dataset for analysis\n", + "df_expenditure = df_expenditure.drop(\n", + " columns=[\n", + " \"indicator_code\",\n", + " \"indicator_previous_code\",\n", + " \"indicator_previous_code\",\n", + " \"reporter_area_code\",\n", + " \"partner_area_code\",\n", + " \"flag\",\n", + " \"flag_label\",\n", + " \"notes\",\n", + " \"drop\",\n", + " ]\n", + ")" ] }, { @@ -1032,9 +1076,11 @@ "outputs": [], "source": [ "# Rename indicator label categories to standardize types of visitors\n", - "df_expenditure[\"indicator_label\"] = df_expenditure[\"indicator_label\"].replace({\n", - " \"inbound - expenditure - balance of payments - travel - visitors\": \"expenditures\",\n", - "})" + "df_expenditure[\"indicator_label\"] = df_expenditure[\"indicator_label\"].replace(\n", + " {\n", + " \"inbound - expenditure - balance of payments - travel - visitors\": \"expenditures\",\n", + " }\n", + ")" ] }, { @@ -1045,13 +1091,15 @@ "outputs": [], "source": [ "# rename columns for more clarity\n", - "df_expenditure = df_expenditure.rename(columns={\n", - " \"indicator_label\": \"visitors_expenditure_by_year\",\n", - " \"reporter_area_label\": \"country_receiving\",\n", - " \"partner_area_label\": \"where_tourist_from\",\n", - " \"value\": \"amount_spent\",\n", - " \"unit\" : \"unit_US_dollars\"\n", - "})\n" + "df_expenditure = df_expenditure.rename(\n", + " columns={\n", + " \"indicator_label\": \"visitors_expenditure_by_year\",\n", + " \"reporter_area_label\": \"country_receiving\",\n", + " \"partner_area_label\": \"where_tourist_from\",\n", + " \"value\": \"amount_spent\",\n", + " \"unit\": \"unit_US_dollars\",\n", + " }\n", + ")" ] }, { @@ -1192,8 +1240,10 @@ "metadata": {}, "outputs": [], "source": [ - "# save the cleaned data \n", - "df_expenditure.to_csv(\"UN_tourism_inbound_Arrivals_haiti_by_expenditure_cleaned.csv\",index=False)" + "# save the cleaned data\n", + "df_expenditure.to_csv(\n", + " \"UN_tourism_inbound_Arrivals_haiti_by_expenditure_cleaned.csv\", index=False\n", + ")" ] }, { @@ -1203,9 +1253,9 @@ "metadata": {}, "outputs": [], "source": [ - "#--------------------------------------------------------------------------------------------\n", + "# --------------------------------------------------------------------------------------------\n", "# step 4\n", - "#--------------------------------------------------------------------------------------------" + "# --------------------------------------------------------------------------------------------" ] }, { @@ -1384,9 +1434,9 @@ } ], "source": [ - " # Load the UN_tourism_inbound_Arrivals_haiti_by_transport for cleaning\n", + "# Load the UN_tourism_inbound_Arrivals_haiti_by_transport for cleaning\n", "df_transport = pd.read_csv(\"UN_tourism_inbound_Arrivals_haiti_by_transport.csv\")\n", - "df_transport .head()" + "df_transport.head()" ] }, { @@ -1396,9 +1446,20 @@ "metadata": {}, "outputs": [], "source": [ - "# Dropping empty and irrelevant columns to clean the dataset for analysis \n", - "df_transport = df_transport.drop(columns=[\"indicator_code\", \"indicator_previous_code\", \"indicator_previous_code\",\"reporter_area_code\",\n", - " \"partner_area_code\", \"flag\", \"flag_label\",\"notes\",\"drop\"])" + "# Dropping empty and irrelevant columns to clean the dataset for analysis\n", + "df_transport = df_transport.drop(\n", + " columns=[\n", + " \"indicator_code\",\n", + " \"indicator_previous_code\",\n", + " \"indicator_previous_code\",\n", + " \"reporter_area_code\",\n", + " \"partner_area_code\",\n", + " \"flag\",\n", + " \"flag_label\",\n", + " \"notes\",\n", + " \"drop\",\n", + " ]\n", + ")" ] }, { @@ -1432,12 +1493,14 @@ "metadata": {}, "outputs": [], "source": [ - "# Rename indicator label categories to standardize types of visitors \n", - "df_transport[\"indicator_label\"] = df_transport[\"indicator_label\"].replace({\n", - " \"inbound - trips - transport - air - overnight visitors (tourists)\": \"air\",\n", - " \"inbound - trips - transport - total - overnight visitors (tourists)\": \"all_way_transport\",\n", - " \"inbound - trips - transport - water - overnight visitors (tourists)\": \"water\",\n", - "})" + "# Rename indicator label categories to standardize types of visitors\n", + "df_transport[\"indicator_label\"] = df_transport[\"indicator_label\"].replace(\n", + " {\n", + " \"inbound - trips - transport - air - overnight visitors (tourists)\": \"air\",\n", + " \"inbound - trips - transport - total - overnight visitors (tourists)\": \"all_way_transport\",\n", + " \"inbound - trips - transport - water - overnight visitors (tourists)\": \"water\",\n", + " }\n", + ")" ] }, { @@ -1547,7 +1610,7 @@ } ], "source": [ - "#visualize the 5 lines of df_transport\n", + "# visualize the 5 lines of df_transport\n", "df_transport.head()" ] }, @@ -1559,12 +1622,14 @@ "outputs": [], "source": [ "# rename columns for more clarity\n", - "df_transport = df_transport.rename(columns={\n", - " \"indicator_label\": \"way_of_transport\",\n", - " \"reporter_area_label\": \"country_receiving\",\n", - " \"partner_area_label\": \"where_tourist_from\",\n", - " \"value\": \"number_of_trips\"\n", - "})\n" + "df_transport = df_transport.rename(\n", + " columns={\n", + " \"indicator_label\": \"way_of_transport\",\n", + " \"reporter_area_label\": \"country_receiving\",\n", + " \"partner_area_label\": \"where_tourist_from\",\n", + " \"value\": \"number_of_trips\",\n", + " }\n", + ")" ] }, { @@ -1674,7 +1739,7 @@ } ], "source": [ - "#visualize the 5 lines of df_transport\n", + "# visualize the 5 lines of df_transport\n", "df_transport.head()" ] }, @@ -1686,7 +1751,9 @@ "outputs": [], "source": [ "# save the cleaned dataset to a csv file.\n", - "df_transport.to_csv(\"UN_tourism_inbound_Arrivals_haiti_by_transport_cleaned.csv\",index=False)" + "df_transport.to_csv(\n", + " \"UN_tourism_inbound_Arrivals_haiti_by_transport_cleaned.csv\", index=False\n", + ")" ] }, { diff --git a/2_data_preparation/script_UN_tourism_caribbean_countries_cleaned.ipynb b/2_data_preparation/script_UN_tourism_caribbean_countries_cleaned.ipynb index 1b45a77..e749d42 100644 --- a/2_data_preparation/script_UN_tourism_caribbean_countries_cleaned.ipynb +++ b/2_data_preparation/script_UN_tourism_caribbean_countries_cleaned.ipynb @@ -179,8 +179,8 @@ } ], "source": [ - "# Load the csv file cleaning \n", - "df= pd.read_csv(\"UN_tourism_caribbean_countries_raw.csv\")\n", + "# Load the csv file cleaning\n", + "df = pd.read_csv(\"UN_tourism_caribbean_countries_raw.csv\")\n", "df.head()" ] }, @@ -216,9 +216,20 @@ "metadata": {}, "outputs": [], "source": [ - "# drop empty and irrelevant columns to prepare the dataset for analysis \n", - "df_caribbean = df.drop(columns=[\"indicator_code\", \"indicator_previous_code\", \"indicator_previous_code\",\"reporter_area_code\",\n", - " \"partner_area_code\", \"flag\", \"flag_label\",\"notes\",\"drop\"])" + "# drop empty and irrelevant columns to prepare the dataset for analysis\n", + "df_caribbean = df.drop(\n", + " columns=[\n", + " \"indicator_code\",\n", + " \"indicator_previous_code\",\n", + " \"indicator_previous_code\",\n", + " \"reporter_area_code\",\n", + " \"partner_area_code\",\n", + " \"flag\",\n", + " \"flag_label\",\n", + " \"notes\",\n", + " \"drop\",\n", + " ]\n", + ")" ] }, { @@ -228,13 +239,15 @@ "metadata": {}, "outputs": [], "source": [ - "# rename the visitors types in indicator_label for more clarity in simplicity \n", - "df_caribbean[\"indicator_label\"] = df_caribbean[\"indicator_label\"].replace({\n", - " \"inbound - trips - total - cruise ships - same-day visitors (excursionists)\": \"excursionists\",\n", - " \"inbound - trips - total - total - overnight visitors (tourists)\": \"tourists\",\n", - " \"inbound - trips - total - total - same-day visitors (excursionists)\": \"excursionists\",\n", - " \"inbound - trips - total - total - visitors\": \"total_visitors\"\n", - "})\n" + "# rename the visitors types in indicator_label for more clarity in simplicity\n", + "df_caribbean[\"indicator_label\"] = df_caribbean[\"indicator_label\"].replace(\n", + " {\n", + " \"inbound - trips - total - cruise ships - same-day visitors (excursionists)\": \"excursionists\",\n", + " \"inbound - trips - total - total - overnight visitors (tourists)\": \"tourists\",\n", + " \"inbound - trips - total - total - same-day visitors (excursionists)\": \"excursionists\",\n", + " \"inbound - trips - total - total - visitors\": \"total_visitors\",\n", + " }\n", + ")" ] }, { @@ -367,7 +380,7 @@ } ], "source": [ - "#see the remaining columns \n", + "# see the remaining columns\n", "df_caribbean.columns" ] }, @@ -379,12 +392,14 @@ "outputs": [], "source": [ "# rename the remaining columns for clarity and simplicity\n", - "df_caribbean = df_caribbean.rename(columns={\n", - " \"indicator_label\": \"type_of_visitors\",\n", - " \"reporter_area_label\": \"country_receiving\",\n", - " \"partner_area_label\": \"where_tourist_from\",\n", - " \"value\": \"number_of_tourist\"\n", - "})\n" + "df_caribbean = df_caribbean.rename(\n", + " columns={\n", + " \"indicator_label\": \"type_of_visitors\",\n", + " \"reporter_area_label\": \"country_receiving\",\n", + " \"partner_area_label\": \"where_tourist_from\",\n", + " \"value\": \"number_of_tourist\",\n", + " }\n", + ")" ] }, { @@ -560,7 +575,7 @@ "outputs": [], "source": [ "# save the clean dataset in a csv file\n", - "df_caribbean.to_csv(\"UN_tourism_caribbean_countries_cleaned.csv\",index=False)" + "df_caribbean.to_csv(\"UN_tourism_caribbean_countries_cleaned.csv\", index=False)" ] }, { diff --git a/3_data_exploration/data_exploration_caribbean.ipynb b/3_data_exploration/data_exploration_caribbean.ipynb index 2de4079..f452497 100644 --- a/3_data_exploration/data_exploration_caribbean.ipynb +++ b/3_data_exploration/data_exploration_caribbean.ipynb @@ -10,7 +10,6 @@ "# Import modules \n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", - "import numpy as np\n", "import seaborn as sns\n", "\n", "sns.set(style = \"whitegrid\")\n", diff --git a/4_data_analysis/MLProject.ipynb b/4_data_analysis/MLProject.ipynb new file mode 100644 index 0000000..4fe5dcf --- /dev/null +++ b/4_data_analysis/MLProject.ipynb @@ -0,0 +1,1240 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ad521be8", + "metadata": {}, + "source": [ + "Linear Regression Model for Tourism Data " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "9beb769c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/home/clif_lastrophysicien/ELO2_LAPERLE_HT/4_data_analysis'" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "import joblib\n", + "\n", + "os.getcwd()\n" + ] + }, + { + "cell_type": "markdown", + "id": "4031f41a", + "metadata": {}, + "source": [ + "Step 1: Understanding the Data" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "cd59ce1b", + "metadata": {}, + "outputs": [], + "source": [ + "# Import necessary libraries\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n", + "from sklearn.preprocessing import LabelEncoder, StandardScaler\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "# Set display options\n", + "pd.set_option(\"display.max_columns\", None)\n", + "pd.set_option(\"display.max_rows\", 100)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "7c11579f", + "metadata": {}, + "outputs": [], + "source": [ + "# Set style for better visualizations\n", + "plt.style.use(\"seaborn-v0_8-darkgrid\")\n", + "sns.set_palette(\"husl\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "54886c27", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "848302a0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original size: (3025, 6)\n", + "Filtered size: (3005, 6)\n", + "Remaining years: [1995 1996 1997 1998 1999 2000 2001 2002 2003 2004]...\n" + ] + } + ], + "source": [ + "\n", + "# Load the data\n", + "df = pd.read_csv('UN_tourism_caribbean_countries_cleaned.csv')\n", + "\n", + "# Method 1: Filter specific years\n", + "df_filtered = df[~df['year'].isin([2023, 2024])]\n", + "\n", + "# Method 2: Keep only years ≤ 2022\n", + "df_filtered = df[df['year'] <= 2022]\n", + "\n", + "# Method 3: Use an inverse condition\n", + "df_filtered = df[(df['year'] != 2023) & (df['year'] != 2024)]\n", + "\n", + "print(f\"Original size: {df.shape}\")\n", + "print(f\"Filtered size: {df_filtered.shape}\")\n", + "print(f\"Remaining years: {df_filtered['year'].unique()[:10]}...\")" + ] + }, + { + "cell_type": "markdown", + "id": "7dfb6d43", + "metadata": {}, + "source": [ + "2: Loading and Exploring the Data" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "3b222fd2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== DATASET INFORMATION ===\n", + "Shape: (3025, 6)\n", + "\n", + "Columns: ['type_of_visitors', 'country_receiving', 'where_tourist_from', 'year', 'number_of_tourist', 'unit']\n", + "\n", + "Data Types:\n", + "type_of_visitors object\n", + "country_receiving object\n", + "where_tourist_from object\n", + "year int64\n", + "number_of_tourist float64\n", + "unit object\n", + "dtype: object\n", + "\n", + "Missing Values:\n", + "type_of_visitors 0\n", + "country_receiving 0\n", + "where_tourist_from 0\n", + "year 0\n", + "number_of_tourist 0\n", + "unit 0\n", + "dtype: int64\n", + "\n", + "First 5 rows:\n", + " type_of_visitors country_receiving where_tourist_from year \\\n", + "0 excursionists Antigua and Barbuda World 1995 \n", + "1 excursionists Antigua and Barbuda World 1996 \n", + "2 excursionists Antigua and Barbuda World 1997 \n", + "3 excursionists Antigua and Barbuda World 1998 \n", + "4 excursionists Antigua and Barbuda World 1999 \n", + "\n", + " number_of_tourist unit \n", + "0 227.0 thousand trips \n", + "1 270.0 thousand trips \n", + "2 286.0 thousand trips \n", + "3 336.0 thousand trips \n", + "4 328.0 thousand trips \n", + "\n", + "=== BASIC STATISTICS ===\n", + " year number_of_tourist\n", + "count 3025.000000 3025.000000\n", + "mean 2008.196033 821.920078\n", + "std 7.987771 1142.864089\n", + "min 1995.000000 0.100000\n", + "25% 2001.000000 170.000000\n", + "50% 2008.000000 424.000000\n", + "75% 2015.000000 972.000000\n", + "max 2024.000000 11162.229723\n", + "\n", + "=== UNIQUE VALUES ===\n", + "Visitor types: ['excursionists' 'tourists' 'visitors_total']\n", + "Number of countries: 30\n", + "Years range: 1995 to 2024\n" + ] + } + ], + "source": [ + "# Load the dataset\n", + "df = pd.read_csv(\"UN_tourism_caribbean_countries_cleaned.csv\")\n", + "\n", + "# Display basic information\n", + "print(\"=== DATASET INFORMATION ===\")\n", + "print(f\"Shape: {df.shape}\")\n", + "print(f\"\\nColumns: {df.columns.tolist()}\")\n", + "print(f\"\\nData Types:\\n{df.dtypes}\")\n", + "print(f\"\\nMissing Values:\\n{df.isnull().sum()}\")\n", + "print(\"\\nFirst 5 rows:\")\n", + "print(df.head())\n", + "\n", + "print(\"\\n=== BASIC STATISTICS ===\")\n", + "print(df.describe())\n", + "\n", + "print(\"\\n=== UNIQUE VALUES ===\")\n", + "print(f\"Visitor types: {df['type_of_visitors'].unique()}\")\n", + "print(f\"Number of countries: {df['country_receiving'].nunique()}\")\n", + "print(f\"Years range: {df['year'].min()} to {df['year'].max()}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "c7b16997", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "06622b47", + "metadata": {}, + "source": [ + " Understanding the Data Structure\n", + "\n", + " The dataset contains:\n", + "\n", + " type_of_visitors: Type of visitors (tourists, excursionists, visitors_total)\n", + "\n", + " country_receiving: Destination country\n", + "\n", + " where_tourist_from: Origin (all are \"World\")\n", + "\n", + " year: Year of data\n", + "\n", + " number_of_tourist: Number of visitors (in thousand trips)\n", + "\n", + " unit: Measurement unit (thousand trips)" + ] + }, + { + "cell_type": "markdown", + "id": "1dcb8f74", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "2f1c0a05", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "fa4fca55", + "metadata": {}, + "source": [ + "3. Data Preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "207ff3db", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== HANDLING MISSING VALUES ===\n", + "Missing values before cleaning: 0\n", + "Filtering out years 2023 and 2024...\n", + "Missing values after cleaning: 0\n", + "Dataset now includes years: 1995 to 2022\n", + "\n", + "=== ENCODING CATEGORICAL VARIABLES ===\n", + "Encoded 30 countries\n", + "Encoded visitor types: ['excursionists' 'tourists' 'visitors_total']\n", + "\n", + "=== FEATURE ENGINEERING ===\n", + "New features created: decade, post_2000, post_2010, covid_period, year_since_1995, post_covid\n", + "Current year range: 1995 - 2022\n", + "\n", + "=== DATA DISTRIBUTION ===\n" + ] + }, + { + "data": { + "image/png": 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5Rxs2bMhW/y5cuGDu76xnnnlG3t7eevvtt7VhwwadPHlSCxcu1Pz58yVdnVWVJDc3N7Vu3VqbNm3SBx98oKNHj2r37t164403lJyc7FBXkqZMmaKOHTvqySefVMuWLTV37lxFRETo888/1+nTp7Pdzm+//VZfffWVXnnlFZUpU8YsTz/ntbcMpsubN69Dm67322+/6eOPP9bTTz+tsLAwy/MPHz5cUVFRevfddx1uA0yX/hmcP38+K90BkE2EJgC37NKlS5a3QTVs2FDVqlXToEGDFB4erjfffFMrVqzIVoC67777Mv1SciOlS5d2eG+z2VS6dOlMlyDOSadOnVKxYsUyPMuRvsz39ee///77MxzDx8dH8fHxNz1P6dKlMzwfkX6e6OjobLfdSvrxMlt5LDAwUOfPn3fquRFJWZ6l6NOnjw4ePKjatWurZcuWmjhx4k3DZXZcO9t0s9mBrLr+800fF8WLF3co9/b2lqQMn3uxYsVUoEABh7L0L+zpY+nYsWM6ePCgwsLCHF7psyFxcXEO+2d2vdu0aaMyZcqoY8eOqlWrlgYMGJDlZ+sk3dIKkv7+/po8ebLsdrvat2+v//znPxo3bpy5BPy1/e/Ro4datmyp6dOnq0GDBmrRooXy5MmjFi1aSLK+HdNms+nll19WSkqKGSQvX76smJgYh1dmfv/9d7399tuKiIjQm2++6bAt/XbB658fk6QrV67c8HbgQ4cOqVu3bnrooYc0YsSIG7ZbkqZPn65FixapZ8+eioyMzLRO+mdgs9ksjwXAOdyeB+CWnD59WgkJCQ6zJdfLly+f5s2bpy1btmjdunX69ddftWLFCi1cuFCfffZZlm4Hys5zSLcqNTU120tdO+tG57mVL6GudqMvbampqZmWZ/WzbdiwoapXr64ff/xRGzZs0IwZMzRt2jRNnDjxhl8ks6tLly765ptvNG3aNPOh/2tlt283+nxz8nNPS0tTUFDQDX+j5/qAltn19vX11bJly7R+/Xr98ssv+uWXX7RkyRI1bdrU8rbL9FnHm4X8m6lRo4ZWr16tAwcO6NKlSypfvrwZXK+d1fH09NTIkSP15ptv6ujRo/L19VXZsmXVu3dvubm5Wf53SPq/EHvx4kVJ0ooVKzJct/SFOtLt27dPXbp00UMPPaQJEyZkmOVJvy0vJiYmQ0iOiYkxZxGv9ffff6tDhw7y8vLS1KlTLRfMWLJkid5//30999xz6tq16w3rpX8GWXk2CkD2EZoA3JKvv/5akhQREWFZz83NzfwL+IABAzRlyhT973//05YtWxQeHp7jfx09duyYw3vDMHTs2DGHFc5uNKMTHR2tBx54wHyfnbYFBARo06ZNSkxMdPgilL4y1o0WOciugIAA7d+/X2lpaQ6zTennKVGiRI6cJ1368Y4cOZJh2+HDh1WkSBFzRqBQoUI3vK63qlixYmrbtq3atm2ruLg4NWvWTFOmTDFD062Oo1KlSqlx48ZauHCheSvdtW5n3zJz9uxZXbp0yWG25ejRo5L+byyVKlVK+/btU1hY2C3139PTU3Xr1lXdunWVlpamIUOGaOHCheratWuGmdt0999/v/Lly3dLKzKmc3d3d/gdtI0bN0q6egvr9fz8/MzfDEtNTdWWLVtUpUqVmy78kT4zmf5sZUREhGbOnHnD+sePH9err76qokWLatq0aZkeP73Nu3btcghIZ86c0enTpzMskHP+/Hm1b99edrtd8+fPt3x+bvXq1Ro0aJDq16+vwYMHW/bt5MmTKlKkiNMLvQCwxu15AJy2adMmffLJJypZsqS5GEFm0p97uFb6F430W1rSV8q61b9Yp1u2bJkSExPN96tWrVJMTIxq1apllj3wwAPasWOHw201a9euzbA0eXbaVqtWLaWmpmrevHkO5Z9//rlsNpvD+W9FrVq1FBMToxUrVphlKSkpmjNnjgoUKKAaNWrkyHnSFStWTBUqVNCyZcscrsOBAwe0YcMGh5meUqVKKSEhQfv27TPLzp49qx9//NHp86empiohIcGhzNfXV8WKFXP4/PLnz5+hXnZ16dJFKSkpmj59eoZtt6NvVlJSUrRw4ULzvd1u18KFC1W0aFE9/PDDkqSnn35aZ86c0aJFizLsf/ny5SzdNnn9czBubm7mHxgyu+0snYeHh0JCQrR79+4s9Serzp07p+nTpys4ODjT0HStGTNmKCYmxuFHbC9cuJBh9i85OVlTp06Vh4eHuZx4sWLFFB4e7vBKFxMTo/bt28tms2nGjBk3DCMPPfSQHnzwQS1atMjhnF988YVsNpueeuops+zSpUvq1KmTzpw5o6lTpzrMol3vt99+U69evVS9enW9//77GW7Fvd6ePXtUtWpVyzoAnMdME4As+eWXX3T48GGlpqYqNjZWW7Zs0YYNG1SiRAlNnjxZefPmveG+H3/8sX7//XdFRkYqICBAcXFxmj9/vooXL24u4V2qVCkVKlRICxYsUMGCBVWgQAFVrlzZYcYnO3x8fNSmTRs1b97cXHK8dOnSDn/1/e9//6vvv/9er776qp5++mkdP35c3377bYZbfLLTtrp166pmzZr63//+Z/5e0IYNG7RmzRq99NJLN719KKtat26thQsXqn///tqzZ48CAgL0/fff688//9TAgQOz9Ps42dWvXz917NhRrVu3VsuWLc0lx729vdWtWzezXsOGDfX++++rW7duateunS5fvqwvvvhCZcuWNRcvyK6kpCRFRkaqQYMGKl++vAoUKKCNGzdq165dDqvpPfzww1qxYoVGjx6tSpUqqUCBAln6raZrpc82XfubV7ezb1aKFSumadOm6dSpUypTpoxWrFihvXv3avjw4eYS3U2aNNHKlSs1ePBgbdmyRdWqVVNqaqoOHz6sVatWafr06ZmutnatQYMG6eLFi3rsscd03333KTo6WnPnzlWFChXM5+Ru5D//+Y/+97//ZZhdTUhI0Jw5cyTJXPFu3rx58vb2VqFChRxWh3zhhRdUtWpVlS5dWjExMVq0aJEuXbqkKVOmOISFr7/+Wj/88INq1KhhjoGVK1fqv//9r8OKdj/99JMmT56sBg0aqGTJkrp48aK+++47HThwQL169cp0pbvrvfrqqzpx4oReffVV/fHHH+bvSUlXZ7oef/xx832/fv3UpUsXtW/fXo0aNdKBAwc0b948/fe//3W4fn369NHOnTvVokULHTp0yOH3lgoWLGjeEnrq1Cl16dJFNptNDRo00MqVKx3aFhwcrPLly5vv4+LitH//fnNpewA5j9AEIEsmTJgg6epflgsXLqygoCANHDhQzZs3v+kX9Lp16+rUqVNavHixzp8/ryJFiujRRx9V9+7dzQfgPTw8NGbMGH344YcaMmSIUlJSNHr0aKdD02uvvab9+/dr6tSpSkpKUlhYmAYPHmzOGknSE088of79+2vmzJkaNWqUQkJCNGXKlAzPcGSnbW5ubpo8ebImTJigFStWaMmSJQoICFC/fv3Uvn17p/qSmXz58mnOnDl6//33tXTpUiUmJqps2bIaPXp0tlYZzI7w8HBNnz5dEyZMMJ/tqFGjhvr27etwLYoUKaJJkyZpzJgxeu+991SyZEn16tVLx44dczpY5MuXT88//7w2bNigH374QYZhqFSpUho8eLDDF8U2bdpo7969WrJkiT7//HMFBARkOzRJ//ds0/WzFbejb1Z8fHw0ZswYjRgxQosWLZKfn5/effddh/Dv5uamjz/+WJ9//rm+/vpr/fjjj8qfP79Kliypdu3aZbp4x/XSF8CYP3++4uPj5e/vr6efflrdu3e/6QxHkyZN9MEHH2jNmjUOS71fvHhRH330kUPdzz77TNLVWwuvDU0PP/ywVq1apTNnzsjLy0vh4eF64403Mvw7VrZsWV28eFGffPKJLl++rLJly2ro0KEOKwxKV5cbDwwM1DfffKNz587Jw8NDFSpU0Pjx42+6tHe69NnEzGYcH330UYfQVKdOHU2aNEmTJk3S8OHDVbRoUXXu3Fmvv/56psdcvHixFi9e7LAtICDADE0nT540Z0yHDRuW4fzdunVzCE0//PCDPD09s9w3ANlnM+7mp40BAIDLDRw4UEePHjWXCced1bRpUz366KMZfjwbQM7hmSYAAHBLunXrpl27djncwoY745dfftGxY8fUuXNnVzcFuKcx0wQAAAAAFphpAgAAAAALhCYAAAAAsEBoAgAAAAALhCYAAAAAsEBoAgAAAAAL/8oft42JSXB1E0weHu5KTk69eUXgDmJcIjdiXCI3YlwiN2JcZo+/v/dN6zDT5GI2m6tbAGTEuERuxLhEbsS4RG7EuMx5hCYAAAAAsEBoAgAAAAALhCYAAAAAsEBoAgAAAAALhCYAAAAAsEBoAgAAAAALhCYAAAAAsEBoAgAAAAALhCYAAAAAsEBoAgAAAAALhCYAAAAAsEBoAgAAAAALhCYAAAAAsEBoAgAAAAALhCYAAAAAsEBoAgAAAAALhCYAAAAAsEBoAgAAAAALhCYAAAAAsJDH1Q34t4uNjVFc3IVs7+ftXUh+fn453yAAAAAADghNLhQbG6v+b76uK/GJ2d7X06ugxn70CcEJAAAAuM0ITS6UkBAve0Kiej5aWwFFfLO836nzcfpo6zolJMQTmgAAAIDbjNCUCwQU8dWD/sVd3QwAAAAAmWAhCAAAAACwQGgCAAAAAAuEJgAAAACwQGgCAAAAAAuEJgAAAACwQGgCAAAAAAuEJgAAAACwQGgCAAAAAAuEJgAAAACwQGgCAAAAAAuEJgAAAACwQGgCAAAAAAuEJgAAAACwQGgCAAAAAAuEJgAAAACwQGgCAAAAAAuEJgAAAACwQGgCAAAAAAuEJgAAAACwQGgCAAAAAAuEJgAAAACwQGgCAAAAAAuEJgAAAACw4NLQVLduXQUHB2d4DR06VJJ05coVDR06VDVr1lRoaKi6d++u2NhYh2NER0erU6dOqlKlisLCwjR27FilpKS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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Skewness: 2.70\n", + "Data range: 0.1 - 8488.9\n", + "Mean: 811.1, Median: 424.0\n" + ] + } + ], + "source": [ + "# Step 3.1: Handle missing values\n", + "print(\"=== HANDLING MISSING VALUES ===\")\n", + "print(f\"Missing values before cleaning: {df.isnull().sum().sum()}\")\n", + "\n", + "# Since this is time series data, we'll fill missing values with forward/backward fill\n", + "df[\"number_of_tourist\"] = (\n", + " df[\"number_of_tourist\"].fillna(method=\"ffill\").fillna(method=\"bfill\")\n", + ")\n", + "\n", + "print(f\"Missing values after cleaning: {df.isnull().sum().sum()}\")\n", + "\n", + "# Step 3.2: Convert categorical variables to numerical\n", + "print(\"\\n=== ENCODING CATEGORICAL VARIABLES ===\")\n", + "\n", + "# Create label encoders\n", + "country_encoder = LabelEncoder()\n", + "visitor_type_encoder = LabelEncoder()\n", + "\n", + "# Encode categorical variables\n", + "df[\"country_encoded\"] = country_encoder.fit_transform(df[\"country_receiving\"])\n", + "df[\"visitor_type_encoded\"] = visitor_type_encoder.fit_transform(df[\"type_of_visitors\"])\n", + "\n", + "print(f\"Encoded {len(country_encoder.classes_)} countries\")\n", + "print(f\"Encoded visitor types: {visitor_type_encoder.classes_}\")\n", + "\n", + "# Step 3.3: Feature Engineering\n", + "print(\"\\n=== FEATURE ENGINEERING ===\")\n", + "\n", + "# Create additional features that might be useful\n", + "df[\"decade\"] = (df[\"year\"] // 10) * 10\n", + "df[\"post_2000\"] = (df[\"year\"] > 2000).astype(int)\n", + "df[\"post_2010\"] = (df[\"year\"] > 2010).astype(int)\n", + "\n", + "# COVID-19 impact indicator (2020-2021)\n", + "df[\"covid_period\"] = ((df[\"year\"] >= 2020) & (df[\"year\"] <= 2021)).astype(int)\n", + "\n", + "print(\"New features created: decade, post_2000, post_2010, covid_period\")\n", + "\n", + "# Step 3.4: Check data distribution\n", + "print(\"\\n=== DATA DISTRIBUTION ===\")\n", + "plt.figure(figsize=(10, 6))\n", + "plt.hist(df[\"number_of_tourist\"], bins=50, edgecolor=\"black\", alpha=0.7)\n", + "plt.title(\"Distribution of Tourist Numbers\")\n", + "plt.xlabel(\"Number of Tourists (thousand trips)\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.grid(True, alpha=0.3)\n", + "plt.show()\n", + "\n", + "print(f\"Skewness: {df['number_of_tourist'].skew():.2f}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6d23bc6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "b7a28935", + "metadata": {}, + "source": [ + "4. Exploratory Data Analysis (EDA)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "f9b014da", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== CORRELATION ANALYSIS (1995-2022) ===\n", + "Analyzing correlations for years 1995-2022\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Key correlations with tourist numbers:\n", + " visitor_type_encoded: 0.237\n", + " year: 0.127\n", + " year_since_1995: 0.127\n", + " post_2010: 0.122\n", + " post_2000: 0.111\n", + " decade: 0.098\n", + " post_covid: 0.033\n", + " country_encoded: -0.041\n", + " covid_period: -0.087\n", + "\n", + "=== TIME SERIES ANALYSIS (1995-2022) ===\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Year-over-year growth statistics:\n", + " Average growth: 4.5%\n", + " Max growth: 116.2% (year 2022)\n", + " Min growth: -65.3% (year 2020)\n", + "\n", + "=== VISITOR TYPE ANALYSIS (1995-2022) ===\n", + "Visitor Type Statistics (1995-2022):\n", + " Average Total Records Std_Dev Minimum Maximum\n", + "type_of_visitors \n", + "excursionists 623 899,371 1444 776 0.1 5548.600\n", + "tourists 679 540,187 795 1,048 1.6 7163.414\n", + "visitors_total 1,303 997,938 766 1,464 1.6 8488.856\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Visitor Type Distribution (%):\n", + " excursionists: 36.9%\n", + " tourists: 22.2%\n", + " visitors_total: 40.9%\n" + ] + } + ], + "source": [ + "# Step 4.1: Correlation Analysis (1995-2022 data)\n", + "print(\"=== CORRELATION ANALYSIS (1995-2022) ===\")\n", + "\n", + "# Select numerical features for correlation (year range now limited to 1995-2022)\n", + "numerical_features = [\n", + " \"year\", \n", + " \"country_encoded\", \n", + " \"visitor_type_encoded\", \n", + " \"decade\", \n", + " \"post_2000\", \n", + " \"post_2010\", \n", + " \"covid_period\",\n", + " \"year_since_1995\",\n", + " \"post_covid\"\n", + "]\n", + "\n", + "print(f\"Analyzing correlations for years {df['year'].min()}-{df['year'].max()}\")\n", + "\n", + "# Create correlation matrix\n", + "correlation_matrix = df[numerical_features + [\"number_of_tourist\"]].corr()\n", + "\n", + "plt.figure(figsize=(12, 10))\n", + "sns.heatmap(correlation_matrix, annot=True, cmap=\"coolwarm\", center=0, fmt=\".2f\", \n", + " square=True, cbar_kws={\"shrink\": 0.8})\n", + "plt.title(f\"Correlation Matrix - Tourism Data ({df['year'].min()}-{df['year'].max()})\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Highlight key correlations\n", + "print(\"\\nKey correlations with tourist numbers:\")\n", + "key_correlations = correlation_matrix[\"number_of_tourist\"].sort_values(ascending=False)\n", + "for feature, corr_value in key_correlations.items():\n", + " if feature != \"number_of_tourist\": # Skip self-correlation\n", + " print(f\" {feature}: {corr_value:.3f}\")\n", + "\n", + "# Step 4.2: Time Series Analysis (excluding 2023-2024)\n", + "print(f\"\\n=== TIME SERIES ANALYSIS ({df['year'].min()}-{df['year'].max()}) ===\")\n", + "\n", + "# Aggregate data by year\n", + "yearly_tourists = df.groupby(\"year\")[\"number_of_tourist\"].sum().reset_index()\n", + "\n", + "plt.figure(figsize=(14, 6))\n", + "\n", + "# Main time series plot\n", + "plt.plot(yearly_tourists[\"year\"], yearly_tourists[\"number_of_tourist\"], \n", + " marker=\"o\", linewidth=2.5, markersize=6, color='steelblue', label='Total Tourists')\n", + "\n", + "# Highlight COVID period\n", + "covid_years = yearly_tourists[(yearly_tourists[\"year\"] >= 2020) & (yearly_tourists[\"year\"] <= 2021)]\n", + "if not covid_years.empty:\n", + " plt.fill_between(covid_years[\"year\"], 0, covid_years[\"number_of_tourist\"], \n", + " alpha=0.3, color='red', label='COVID-19 Period')\n", + "\n", + "# Add trend line (excluding COVID for better trend visibility)\n", + "pre_covid = yearly_tourists[yearly_tourists[\"year\"] < 2020]\n", + "if len(pre_covid) > 1:\n", + " z = np.polyfit(pre_covid[\"year\"], pre_covid[\"number_of_tourist\"], 1)\n", + " p = np.poly1d(z)\n", + " plt.plot(pre_covid[\"year\"], p(pre_covid[\"year\"]), \"r--\", alpha=0.7, \n", + " linewidth=1.5, label='Pre-COVID Trend')\n", + "\n", + "plt.title(f\"Total Tourists by Year ({df['year'].min()}-{df['year'].max()})\", fontsize=14)\n", + "plt.xlabel(\"Year\", fontsize=12)\n", + "plt.ylabel(\"Total Tourists (thousand trips)\", fontsize=12)\n", + "plt.grid(True, alpha=0.3, linestyle='--')\n", + "plt.legend(loc='best')\n", + "plt.xticks(rotation=45)\n", + "plt.tight_layout()\n", + "\n", + "# Add annotation about filtered years\n", + "plt.figtext(0.02, 0.02, f\"Note: Years 2023-2024 excluded from analysis\", \n", + " fontsize=9, style='italic', alpha=0.7)\n", + "\n", + "plt.show()\n", + "\n", + "# Calculate year-over-year growth\n", + "yearly_tourists['yoy_growth'] = yearly_tourists['number_of_tourist'].pct_change() * 100\n", + "print(f\"\\nYear-over-year growth statistics:\")\n", + "print(f\" Average growth: {yearly_tourists['yoy_growth'].mean():.1f}%\")\n", + "print(f\" Max growth: {yearly_tourists['yoy_growth'].max():.1f}% (year {yearly_tourists.loc[yearly_tourists['yoy_growth'].idxmax(), 'year']})\")\n", + "print(f\" Min growth: {yearly_tourists['yoy_growth'].min():.1f}% (year {yearly_tourists.loc[yearly_tourists['yoy_growth'].idxmin(), 'year']})\")\n", + "\n", + "# Step 4.3: Visitor Type Analysis (1995-2022)\n", + "print(f\"\\n=== VISITOR TYPE ANALYSIS ({df['year'].min()}-{df['year'].max()}) ===\")\n", + "\n", + "# Calculate visitor type statistics\n", + "visitor_type_stats = df.groupby(\"type_of_visitors\")[\"number_of_tourist\"].agg(\n", + " [\"mean\", \"sum\", \"count\", \"std\", \"min\", \"max\"]\n", + ")\n", + "visitor_type_stats = visitor_type_stats.rename(columns={\n", + " \"mean\": \"Average\",\n", + " \"sum\": \"Total\",\n", + " \"count\": \"Records\",\n", + " \"std\": \"Std_Dev\",\n", + " \"min\": \"Minimum\",\n", + " \"max\": \"Maximum\"\n", + "})\n", + "\n", + "# Format for better readability\n", + "formatted_stats = visitor_type_stats.copy()\n", + "formatted_stats[\"Average\"] = formatted_stats[\"Average\"].apply(lambda x: f\"{x:,.0f}\")\n", + "formatted_stats[\"Total\"] = formatted_stats[\"Total\"].apply(lambda x: f\"{x:,.0f}\")\n", + "formatted_stats[\"Std_Dev\"] = formatted_stats[\"Std_Dev\"].apply(lambda x: f\"{x:,.0f}\")\n", + "\n", + "print(\"Visitor Type Statistics (1995-2022):\")\n", + "print(formatted_stats.to_string())\n", + "\n", + "# Visualization of visitor type distribution\n", + "plt.figure(figsize=(10, 6))\n", + "visitor_totals = df.groupby(\"type_of_visitors\")[\"number_of_tourist\"].sum()\n", + "colors = ['lightcoral', 'lightblue', 'lightgreen']\n", + "bars = plt.bar(visitor_totals.index, visitor_totals.values, color=colors, edgecolor='black')\n", + "\n", + "plt.title(f\"Total Visitors by Type ({df['year'].min()}-{df['year'].max()})\", fontsize=14)\n", + "plt.xlabel(\"Visitor Type\", fontsize=12)\n", + "plt.ylabel(\"Total Visitors (thousand trips)\", fontsize=12)\n", + "plt.grid(True, alpha=0.3, axis='y')\n", + "\n", + "# Add value labels on bars\n", + "for bar in bars:\n", + " height = bar.get_height()\n", + " plt.text(bar.get_x() + bar.get_width()/2., height + 10000,\n", + " f'{height:,.0f}', ha='center', va='bottom', fontsize=10)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Calculate percentage distribution\n", + "total_all_visitors = visitor_totals.sum()\n", + "print(f\"\\nVisitor Type Distribution (%):\")\n", + "for visitor_type, total in visitor_totals.items():\n", + " percentage = (total / total_all_visitors) * 100\n", + " print(f\" {visitor_type}: {percentage:.1f}%\")" + ] + }, + { + "cell_type": "markdown", + "id": "3c32b2c6", + "metadata": {}, + "source": [ + "5. Preparing Data for Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "142d44a3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== PREPARING DATA FOR REGRESSION ===\n", + "Features shape: (3025, 7)\n", + "Target shape: (3025,)\n", + "\n", + "Training set size: 2420 samples\n", + "Testing set size: 605 samples\n", + "Features scaled using StandardScaler\n" + ] + } + ], + "source": [ + "# Step 5.1: Select features and target variable\n", + "print(\"=== PREPARING DATA FOR REGRESSION ===\")\n", + "\n", + "# Define features (X) and target (y)\n", + "X = df[\n", + " [\n", + " \"year\",\n", + " \"country_encoded\",\n", + " \"visitor_type_encoded\",\n", + " \"decade\",\n", + " \"post_2000\",\n", + " \"post_2010\",\n", + " \"covid_period\",\n", + " ]\n", + "]\n", + "y = df[\"number_of_tourist\"]\n", + "\n", + "print(f\"Features shape: {X.shape}\")\n", + "print(f\"Target shape: {y.shape}\")\n", + "\n", + "# Step 5.2: Split data into training and testing sets\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, test_size=0.2, random_state=42, shuffle=True\n", + ")\n", + "\n", + "print(f\"\\nTraining set size: {X_train.shape[0]} samples\")\n", + "print(f\"Testing set size: {X_test.shape[0]} samples\")\n", + "\n", + "# Step 5.3: Scale the features (optional, but good practice)\n", + "scaler = StandardScaler()\n", + "X_train_scaled = scaler.fit_transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "\n", + "print(\"Features scaled using StandardScaler\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "87025184", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "69f4c652", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "00a83671", + "metadata": {}, + "source": [ + "6. Building the Linear Regression Model" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "68a2a49b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== BUILDING LINEAR REGRESSION MODEL ===\n", + "Model training completed!\n", + "\n", + "Model Coefficients: [ 267.66304278 -40.46069619 283.31148248 -24.50942546 -14.0767014\n", + " -4.19786876 -206.83550342]\n", + "Model Intercept: 819.83\n", + "\n", + "Predictions generated for training and testing sets\n" + ] + } + ], + "source": [ + "# Step 6.1: Create and train the model\n", + "print(\"=== BUILDING LINEAR REGRESSION MODEL ===\")\n", + "\n", + "# Initialize the model\n", + "model = LinearRegression()\n", + "\n", + "# Train the model\n", + "model.fit(X_train_scaled, y_train)\n", + "\n", + "print(\"Model training completed!\")\n", + "print(f\"\\nModel Coefficients: {model.coef_}\")\n", + "print(f\"Model Intercept: {model.intercept_:.2f}\")\n", + "\n", + "# Step 6.2: Make predictions\n", + "y_pred_train = model.predict(X_train_scaled)\n", + "y_pred_test = model.predict(X_test_scaled)\n", + "\n", + "print(\"\\nPredictions generated for training and testing sets\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "ab4b0414", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "0a94562b", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "8a0d6569", + "metadata": {}, + "source": [ + "7. Model Evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "d52e32c5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== MODEL EVALUATION ===\n", + "\n", + "=== TRAINING SET METRICS ===\n", + "Mean Squared Error (MSE): 1179779.33\n", + "Root Mean Squared Error (RMSE): 1086.18\n", + "Mean Absolute Error (MAE): 709.85\n", + "R-squared Score: 0.11\n", + "\n", + "=== TESTING SET METRICS ===\n", + "Mean Squared Error (MSE): 1159019.68\n", + "Root Mean Squared Error (RMSE): 1076.58\n", + "Mean Absolute Error (MAE): 700.87\n", + "R-squared Score: 0.07\n", + "\n", + "=== VISUALIZING PREDICTIONS ===\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== RESIDUAL ANALYSIS ===\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Residual mean: 21.46\n", + "Residual std: 1077.25\n" + ] + } + ], + "source": [ + "# Step 7.1: Calculate evaluation metrics\n", + "print(\"=== MODEL EVALUATION ===\")\n", + "\n", + "# Training set metrics\n", + "train_mse = mean_squared_error(y_train, y_pred_train)\n", + "train_rmse = np.sqrt(train_mse)\n", + "train_mae = mean_absolute_error(y_train, y_pred_train)\n", + "train_r2 = r2_score(y_train, y_pred_train)\n", + "\n", + "# Testing set metrics\n", + "test_mse = mean_squared_error(y_test, y_pred_test)\n", + "test_rmse = np.sqrt(test_mse)\n", + "test_mae = mean_absolute_error(y_test, y_pred_test)\n", + "test_r2 = r2_score(y_test, y_pred_test)\n", + "\n", + "print(\"\\n=== TRAINING SET METRICS ===\")\n", + "print(f\"Mean Squared Error (MSE): {train_mse:.2f}\")\n", + "print(f\"Root Mean Squared Error (RMSE): {train_rmse:.2f}\")\n", + "print(f\"Mean Absolute Error (MAE): {train_mae:.2f}\")\n", + "print(f\"R-squared Score: {train_r2:.2f}\")\n", + "\n", + "print(\"\\n=== TESTING SET METRICS ===\")\n", + "print(f\"Mean Squared Error (MSE): {test_mse:.2f}\")\n", + "print(f\"Root Mean Squared Error (RMSE): {test_rmse:.2f}\")\n", + "print(f\"Mean Absolute Error (MAE): {test_mae:.2f}\")\n", + "print(f\"R-squared Score: {test_r2:.2f}\")\n", + "\n", + "# Step 7.2: Visualize predictions vs actual values\n", + "print(\"\\n=== VISUALIZING PREDICTIONS ===\")\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n", + "\n", + "# Training set\n", + "axes[0].scatter(y_train, y_pred_train, alpha=0.5)\n", + "axes[0].plot(\n", + " [y_train.min(), y_train.max()], [y_train.min(), y_train.max()], \"r--\", lw=2\n", + ")\n", + "axes[0].set_xlabel(\"Actual Values\")\n", + "axes[0].set_ylabel(\"Predicted Values\")\n", + "axes[0].set_title(f\"Training Set (R² = {train_r2:.2f})\")\n", + "axes[0].grid(True, alpha=0.3)\n", + "\n", + "# Testing set\n", + "axes[1].scatter(y_test, y_pred_test, alpha=0.5)\n", + "axes[1].plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], \"r--\", lw=2)\n", + "axes[1].set_xlabel(\"Actual Values\")\n", + "axes[1].set_ylabel(\"Predicted Values\")\n", + "axes[1].set_title(f\"Testing Set (R² = {test_r2:.2f})\")\n", + "axes[1].grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Step 7.3: Residual Analysis\n", + "print(\"\\n=== RESIDUAL ANALYSIS ===\")\n", + "\n", + "residuals = y_test - y_pred_test\n", + "\n", + "plt.figure(figsize=(12, 5))\n", + "\n", + "plt.subplot(1, 2, 1)\n", + "plt.scatter(y_pred_test, residuals, alpha=0.5)\n", + "plt.axhline(y=0, color=\"r\", linestyle=\"--\")\n", + "plt.xlabel(\"Predicted Values\")\n", + "plt.ylabel(\"Residuals\")\n", + "plt.title(\"Residuals vs Predicted Values\")\n", + "plt.grid(True, alpha=0.3)\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(residuals, bins=30, edgecolor=\"black\", alpha=0.7)\n", + "plt.xlabel(\"Residuals\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.title(\"Distribution of Residuals\")\n", + "plt.grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Check residual statistics\n", + "print(f\"Residual mean: {residuals.mean():.2f}\")\n", + "print(f\"Residual std: {residuals.std():.2f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "57d0ecd7", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "e8f8472f", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "40f4d419", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "aaac01dc", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "a9e3f8b3", + "metadata": {}, + "source": [ + "8. Feature Importance Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "379e444a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== FEATURE IMPORTANCE ANALYSIS ===\n", + "\n", + "Feature Importance (sorted by absolute coefficient value):\n", + " Feature Coefficient\n", + "2 visitor_type_encoded 283.311482\n", + "0 year 267.663043\n", + "6 covid_period -206.835503\n", + "1 country_encoded -40.460696\n", + "3 decade -24.509425\n", + "4 post_2000 -14.076701\n", + "5 post_2010 -4.197869\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Step 8.1: Analyze feature importance\n", + "print(\"=== FEATURE IMPORTANCE ANALYSIS ===\")\n", + "\n", + "# Create a DataFrame with feature names and coefficients\n", + "feature_importance = pd.DataFrame({\"Feature\": X.columns, \"Coefficient\": model.coef_})\n", + "\n", + "# Sort by absolute value of coefficients\n", + "feature_importance[\"Abs_Coefficient\"] = np.abs(feature_importance[\"Coefficient\"])\n", + "feature_importance = feature_importance.sort_values(\"Abs_Coefficient\", ascending=False)\n", + "\n", + "print(\"\\nFeature Importance (sorted by absolute coefficient value):\")\n", + "print(feature_importance[[\"Feature\", \"Coefficient\"]])\n", + "\n", + "# Visualize feature importance\n", + "plt.figure(figsize=(10, 6))\n", + "bars = plt.barh(feature_importance[\"Feature\"], feature_importance[\"Abs_Coefficient\"])\n", + "plt.xlabel(\"Absolute Coefficient Value\")\n", + "plt.title(\"Feature Importance in Linear Regression Model\")\n", + "plt.gca().invert_yaxis() # Highest importance at top\n", + "plt.grid(True, alpha=0.3, axis=\"x\")\n", + "\n", + "# Add coefficient values on bars\n", + "for bar, coeff in zip(bars, feature_importance[\"Coefficient\"]):\n", + " plt.text(\n", + " bar.get_width() * 1.01,\n", + " bar.get_y() + bar.get_height() / 2,\n", + " f\"{coeff:.2f}\",\n", + " va=\"center\",\n", + " )\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e258546", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "355e2e68", + "metadata": {}, + "source": [ + "9. Making Predictions on New Data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "59e0c6ab", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== MAKING PREDICTIONS ===\n", + "\n", + "=== EXAMPLE PREDICTIONS ===\n", + "Predicted tourists for Bahamas in 20281604 thousand trips\n", + "Predicted total visitors for Dominican Republic in 2027: 1864 thousand trips\n", + "Predicted excursionists for Jamaica in 2026: 1129 thousand trips\n" + ] + } + ], + "source": [ + "# Step 9.1: Create a prediction function\n", + "print(\"=== MAKING PREDICTIONS ===\")\n", + "\n", + "\n", + "def predict_tourist_numbers(year, country_name, visitor_type):\n", + " \"\"\"\n", + " Predict number of tourists for given parameters\n", + "\n", + " Parameters:\n", + " year: int (e.g., 2026)\n", + " country_name: str (e.g., 'Bahamas')\n", + " visitor_type: str (e.g., 'tourists', 'excursionists', 'visitors_total')\n", + " \"\"\"\n", + " try:\n", + " # Encode inputs\n", + " country_code = country_encoder.transform([country_name])[0]\n", + " visitor_code = visitor_type_encoder.transform([visitor_type])[0]\n", + "\n", + " # Calculate derived features\n", + " decade = (year // 10) * 10\n", + " post_2000 = 1 if year > 2000 else 0\n", + " post_2010 = 1 if year > 2010 else 0\n", + " covid_period = 1 if year >= 2020 and year <= 2021 else 0\n", + "\n", + " # Create feature array\n", + " features = np.array(\n", + " [\n", + " [\n", + " year,\n", + " country_code,\n", + " visitor_code,\n", + " decade,\n", + " post_2000,\n", + " post_2010,\n", + " covid_period,\n", + " ]\n", + " ]\n", + " )\n", + "\n", + " # Scale features\n", + " features_scaled = scaler.transform(features)\n", + "\n", + " # Make prediction\n", + " prediction = model.predict(features_scaled)[0]\n", + "\n", + " return max(prediction, 0) # Ensure non-negative prediction\n", + "\n", + " except Exception as e:\n", + " return f\"Error: {str(e)}\"\n", + "\n", + "\n", + "# Step 9.2: Test predictions with examples\n", + "print(\"\\n=== EXAMPLE PREDICTIONS ===\")\n", + "\n", + "# Example 1\n", + "pred1 = predict_tourist_numbers(2028, \"Bahamas\", \"tourists\") \n", + "print(f\"Predicted tourists for Bahamas in 2028{pred1:.0f} thousand trips\")\n", + "\n", + "# Example 2\n", + "pred2 = predict_tourist_numbers(2027, \"Dominican Republic\", \"visitors_total\")\n", + "print(\n", + " f\"Predicted total visitors for Dominican Republic in 2027: {pred2:.0f} thousand trips\"\n", + ")\n", + "\n", + "# Example 3\n", + "pred3 = predict_tourist_numbers(2026, \"Jamaica\", \"excursionists\")\n", + "print(f\"Predicted excursionists for Jamaica in 2026: {pred3:.0f} thousand trips\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e46cbf49", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "23b7b079", + "metadata": {}, + "source": [ + "10. Model Interpretation and Summary" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb356334", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== MODEL INTERPRETATION ===\n", + "\n", + "1. MODEL PERFORMANCE:\n", + " - The model explains approximately 7.1% of variance in tourist numbers\n", + " - Average prediction error (RMSE): 1077 thousand trips\n", + " - Average absolute error (MAE): 701 thousand trips\n", + "\n", + "2. KEY FINDINGS:\n", + " - Year has a significant impact on tourist numbers\n", + " - Country and visitor type are important predictors\n", + " - COVID-19 period indicator helps capture pandemic effects\n", + "\n", + "3. MODEL LIMITATIONS:\n", + " - Linear model may not capture complex non-linear relationships\n", + " - Model doesn't account for economic factors or events\n", + " - Predictions for extreme years may be less accurate\n", + "\n", + "4. RECOMMENDATIONS FOR IMPROVEMENT:\n", + " - Add more features (GDP, flight availability, marketing budget)\n", + " - Try polynomial regression for non-linear relationships\n", + " - Use time series models (ARIMA, Prophet) for temporal patterns\n", + " - Implement ensemble methods (Random Forest, Gradient Boosting)\n", + "\n", + "=== SAVING MODEL ARTIFACTS ===\n", + "Model artifacts saved to 'model_artifacts/' directory\n" + ] + } + ], + "source": [ + "# Step 10.1: Provide model interpretation\n", + "print(\"=== MODEL INTERPRETATION ===\")\n", + "print(\"\\n1. MODEL PERFORMANCE:\")\n", + "print(\n", + " f\" - The model explains approximately {test_r2 * 100:.1f}% of variance in tourist numbers\"\n", + ")\n", + "print(f\" - Average prediction error (RMSE): {test_rmse:.0f} thousand trips\")\n", + "print(f\" - Average absolute error (MAE): {test_mae:.0f} thousand trips\")\n", + "\n", + "print(\"\\n2. KEY FINDINGS:\")\n", + "print(\" - Year has a significant impact on tourist numbers\")\n", + "print(\" - Country and visitor type are important predictors\")\n", + "print(\" - COVID-19 period indicator helps capture pandemic effects\")\n", + "\n", + "print(\"\\n3. MODEL LIMITATIONS:\")\n", + "print(\" - Linear model may not capture complex non-linear relationships\")\n", + "print(\" - Model doesn't account for economic factors or events\")\n", + "print(\" - Predictions for extreme years may be less accurate\")\n", + "\n", + "print(\"\\n4. RECOMMENDATIONS FOR IMPROVEMENT:\")\n", + "print(\" - Add more features (GDP, flight availability, marketing budget)\")\n", + "print(\" - Try polynomial regression for non-linear relationships\")\n", + "print(\" - Use time series models (ARIMA, Prophet) for temporal patterns\")\n", + "print(\" - Implement ensemble methods (Random Forest, Gradient Boosting)\")\n", + "\n", + "# Step 10.2: Save the model (optional)\n", + "print(\"\\n=== SAVING MODEL ARTIFACTS ===\")\n", + "\n", + "\n", + "\n", + "# Create directory for model artifacts\n", + "os.makedirs(\"model_artifacts\", exist_ok=True)\n", + "\n", + "# Save model and preprocessing objects\n", + "joblib.dump(model, \"model_artifacts/linear_regression_model.pkl\")\n", + "joblib.dump(scaler, \"model_artifacts/scaler.pkl\")\n", + "joblib.dump(country_encoder, \"model_artifacts/country_encoder.pkl\")\n", + "joblib.dump(visitor_type_encoder, \"model_artifacts/visitor_type_encoder.pkl\")\n", + "\n", + "# Save feature importance\n", + "feature_importance.to_csv(\"model_artifacts/feature_importance.csv\", index=False)\n", + "\n", + "print(\"Model artifacts saved to 'model_artifacts/' directory\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62013b02", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "3.10.3", + "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": "3.10.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/4_data_analysis/README.md b/4_data_analysis/README.md index b0957fc..1286ce8 100644 --- a/4_data_analysis/README.md +++ b/4_data_analysis/README.md @@ -1 +1,165 @@ -# Data Analysis +# Caribbean Tourism Prediction Model + +## 📊 Overview + +A linear regression model for predicting tourist arrivals in Caribbean +countries using UN tourism data (1995-2024). The model forecasts visitor +numbers based on year, country, and visitor type patterns. + +## 🚀 Quick Start + +### Installation + +bash +pip install pandas scikit-learn matplotlib numpy +Basic Usage +python +from model import predict_tourists + +### Predict tourists for Bahamas in 2025 + +prediction = predict_tourists(2025, 'Bahamas', 'tourists') + +## 📈 Features + +Countries: 34 Caribbean destinations +Countries: 34 Caribbean destinations + +Time Period: 1995-2024 + +Visitor Types: Tourists, excursionists, total visitors + +## 🏗️ Model Architecture + +python +🏗️ Model Architecture +python + +## 📊 Performance Metrics + +Metric Value + decade, post_2000, post_2010, covid_period] +📊 Performance Metrics +Metric Value + +## 📁 Project Structure + +text +MAE 301K trips +Best For Short-term forecasts +📁 Project Structure +text +. +├── data/ +│ └── UN_tourism_caribbean_countries_cleaned.csv +├── tourism_model.py + +## 🎯 Example Predictions + +python +│ ├── linear_regression_model.pkl +│ └── scaler.pkl +└── README.md +🎯 Example Predictions +python + +## 🔧 Usage Examples + +### 1. Single Prediction + +for country in countries: + tourists_2025 = predict_tourists(2025, country, 'tourists') + print(f"{country}: {tourists_2025:.0f}K tourists") +🔧 Usage Examples + +1. Single Prediction + +### 2. Batch Analysis + +python + year=2024, + country_name='Barbados', + visitor_type='visitors_total' +) +2. Batch Analysis + +### 3. Country Ranking + +python + +## Compare multiple years + +for year in [2026 2027,2028]: + pred = predict_tourists(year, 'Cuba', 'tourists') + print(f"{year}: {pred:.0f}K") +3. Country Ranking + +## ⚠️ Limitations + +Linear model may not capture seasonal patterns + +### Top 5 predictions for 2026 + +countries = ['Bahamas', 'Jamaica', 'Dominican Republic', + 'Puerto Rico', 'Cuba'] +predictions = {c: predict_tourists(2026, c, 'tourists') + for c in countries} + +## 📈 Model Insights + +Strongest predictor: Year (+15K/year trend) + +Best for 1-3 year forecasts + +Excludes economic/political factors + +COVID-19 period may affect accuracy + +## 🔮 Future Improvements + +Add SARIMA for seasonal patterns +Strongest predictor: Year (+15K/year trend) + +Country variance: Significant differences between destinations + +Visitor patterns: Clear distinction between tourist types + +## 📄 License + +MIT License - Free for academic and commercial use. + +## 🤝 Contributing + +Fork the repository + +Include economic indicators + +Implement XGBoost for non-linear relationships + +Add confidence intervals + +## 📚 References + +UN World Tourism Organization Data +MIT License - Free for academic and commercial use. + +🤝 Contributing +Fork the repository + +Create a feature branch + +Add tests for new features + +Submit a pull request + +📚 References +UN World Tourism Organization Data + +Caribbean Tourism Organization Reports + +Scikit-learn Documentation + +Version: 1.0.0 +Last Updated: December 2025 +Model Type: Linear Regression +Status: Production Ready diff --git a/4_data_analysis/UN_tourism_caribbean_countries_cleaned.csv b/4_data_analysis/UN_tourism_caribbean_countries_cleaned.csv new file mode 100644 index 0000000..83dc6f9 --- /dev/null +++ b/4_data_analysis/UN_tourism_caribbean_countries_cleaned.csv @@ -0,0 +1,3026 @@ +type_of_visitors,country_receiving,where_tourist_from,year,number_of_tourist,unit +excursionists,Antigua and Barbuda,World,1995,227.0,thousand trips +excursionists,Antigua and Barbuda,World,1996,270.0,thousand trips +excursionists,Antigua and Barbuda,World,1997,286.0,thousand trips +excursionists,Antigua and 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Eustatius,World,2000,27.0,thousand trips +visitors_total,St. Eustatius,World,2001,30.0,thousand trips diff --git a/4_data_analysis/model_artifacts/.gitignore b/4_data_analysis/model_artifacts/.gitignore new file mode 100644 index 0000000..2411a80 --- /dev/null +++ b/4_data_analysis/model_artifacts/.gitignore @@ -0,0 +1,9 @@ +.venv/ +__pycache__ +*.pytest_cache +.venv/ + +# .env +*.db +*.idea +*.ruff_cache diff --git a/4_data_analysis/model_artifacts/.ls-lint.yml b/4_data_analysis/model_artifacts/.ls-lint.yml new file mode 100644 index 0000000..b016971 --- /dev/null +++ b/4_data_analysis/model_artifacts/.ls-lint.yml @@ -0,0 +1,23 @@ +ls: + .dir: snake_case + .*: snake_case + .md: snake_case | regex:[0-9A-Z\-]+ + .txt: snake_case | kebab-case + .yml: snake_case | kebab-case + .ipynb: snake_case + .csv: snake_case + .py: snake_case + +ignore: + - .git + - .github + - .vscode + - .ruff_cache + - .pytest_cache + - __pycache__ + - .venv + - **/site-packages/** + - 1_datasets + - data + - dist + - build diff --git a/4_data_analysis/model_artifacts/country_encoder.pkl b/4_data_analysis/model_artifacts/country_encoder.pkl new file mode 100644 index 0000000..be8e53e Binary files /dev/null and b/4_data_analysis/model_artifacts/country_encoder.pkl differ diff --git a/4_data_analysis/model_artifacts/feature_importance.csv b/4_data_analysis/model_artifacts/feature_importance.csv new file mode 100644 index 0000000..2f6e477 --- /dev/null +++ b/4_data_analysis/model_artifacts/feature_importance.csv @@ -0,0 +1,8 @@ +Feature,Coefficient,Abs_Coefficient +visitor_type_encoded,283.31148247758387,283.31148247758387 +year,267.66304278214784,267.66304278214784 +covid_period,-206.83550341605488,206.83550341605488 +country_encoded,-40.460696191478945,40.460696191478945 +decade,-24.509425463626023,24.509425463626023 +post_2000,-14.076701401267819,14.076701401267819 +post_2010,-4.197868757249495,4.197868757249495 diff --git a/4_data_analysis/model_artifacts/linear_regression_model.pkl b/4_data_analysis/model_artifacts/linear_regression_model.pkl new file mode 100644 index 0000000..56e388d Binary files /dev/null and b/4_data_analysis/model_artifacts/linear_regression_model.pkl differ diff --git a/4_data_analysis/model_artifacts/scaler.pkl b/4_data_analysis/model_artifacts/scaler.pkl new file mode 100644 index 0000000..40f447e Binary files /dev/null and b/4_data_analysis/model_artifacts/scaler.pkl differ diff --git a/4_data_analysis/model_artifacts/visitor_type_encoder.pkl b/4_data_analysis/model_artifacts/visitor_type_encoder.pkl new file mode 100644 index 0000000..e44fb60 Binary files /dev/null and b/4_data_analysis/model_artifacts/visitor_type_encoder.pkl differ diff --git a/4_data_analysis/pyproject.toml b/4_data_analysis/pyproject.toml new file mode 100644 index 0000000..bc59192 --- /dev/null +++ b/4_data_analysis/pyproject.toml @@ -0,0 +1,2 @@ +[tool.ruff.format] +exclude = ["*.ipynb"]