diff --git a/Salary_Analytics_Task.ipynb b/Salary_Analytics_Task.ipynb new file mode 100644 index 0000000..319bbce --- /dev/null +++ b/Salary_Analytics_Task.ipynb @@ -0,0 +1,5091 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "collapsed_sections": [ + "pvZF9BMSzAQ7" + ] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "## 1. Setup and Data Connection" + ], + "metadata": { + "id": "3y-QjCv8KJoo" + } + }, + { + "cell_type": "markdown", + "source": [ + "### 1.1 Database Connection Setup" + ], + "metadata": { + "id": "pvZF9BMSzAQ7" + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XuJLa6e8BKn5", + "outputId": "209a2064-b797-4ccb-bbb0-8cc14dcb8eb1" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m15.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h" + ] + } + ], + "source": [ + "!pip install sqlalchemy psycopg2-binary -qq" + ] + }, + { + "cell_type": "code", + "source": [ + "from sqlalchemy import create_engine, text\n", + "\n", + "# database credentials\n", + "DB_USER = \"salaryloan\"\n", + "DB_PASSWORD = \"salaryloan\"\n", + "DB_HOST = \"dev-data.simbrellang.net\"\n", + "DB_PORT = \"10532\"\n", + "DB_NAME = \"salaryloan\"\n", + "\n", + "# SQLAlchemy connection URL\n", + "DATABASE_URL = f\"postgresql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}:{DB_PORT}/{DB_NAME}\"\n", + "\n", + "# Create the database engine\n", + "engine = create_engine(DATABASE_URL)\n", + "\n", + "# Test the connection\n", + "try:\n", + " with engine.connect() as conn:\n", + " result = conn.execute(text(\"SELECT version();\"))\n", + " print(\"Connected successfully!\")\n", + " print(result.fetchone()) # Print PostgreSQL version\n", + "except Exception as e:\n", + " print(\"Error connecting to database:\", e)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "B9tkEDTDBNQW", + "outputId": "a7986644-80e4-488f-97ae-0d9d9dee3eb5" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Connected successfully!\n", + "('PostgreSQL 16.8 (Ubuntu 16.8-0ubuntu0.24.04.1) on x86_64-pc-linux-gnu, compiled by gcc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0, 64-bit',)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 1.2 Data Loading\n" + ], + "metadata": { + "id": "gWncpI3TzU1t" + } + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "import calendar\n", + "from datetime import datetime\n", + "\n", + "table_name = \"customer_account_transaction_hx\"\n", + "\n", + "# Load data into a Pandas DataFrame\n", + "df = pd.read_sql(f\"SELECT * FROM {table_name}\", engine)\n", + "\n", + "# Display the first few rows\n", + "display(df.head(20))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "XTThey6EB0U1", + "outputId": "fc3db25e-e988-4e42-fe6a-ceca293b8bfa" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + " id accountid trx_start_date trx_end_date amount d1 d2 d3 \\\n", + "0 1335300 1945204013 2024-06-12 2024-06-12 54.0 T BI D \n", + "1 1335301 1945204013 2024-06-16 2024-06-16 52500.0 T BI C \n", + "2 1335302 1945204013 2024-06-15 2024-06-15 505.0 T BI D \n", + "3 1335303 1945204013 2024-06-17 2024-06-17 5350.0 T CI C \n", + "4 1335304 1945204013 2024-06-13 2024-06-13 10000.0 T CI D \n", + "5 1335305 2184939014 2024-06-17 2024-06-17 45150.0 T CI C \n", + "6 1335306 2184939014 2024-06-16 2024-06-16 2160.0 T CI C \n", + "7 1335307 2184939014 2024-06-13 2024-06-13 55640.0 T BI C \n", + "8 1335308 2184939014 2024-06-12 2024-06-12 53.5 T BI D \n", + "9 1335309 2184939014 2024-06-12 2024-06-12 10100.0 T CI D \n", + "10 1335310 2184939014 2024-06-14 2024-06-14 20800.0 T CI D \n", + "11 1335311 2184939014 2024-06-12 2024-06-12 21600.0 T CI D \n", + "12 1335312 2184939014 2024-06-15 2024-06-15 21000.0 T CI D \n", + "13 1335313 1757258011 2024-06-13 2024-06-13 104.0 T CI D \n", + "14 1335314 1757258011 2024-06-15 2024-06-15 330.0 T CI D \n", + "15 1335315 1757258011 2024-06-16 2024-06-16 107.0 T CI D \n", + "16 1335316 1757258011 2024-06-13 2024-06-13 105.0 T CI D \n", + "17 1335317 1757258011 2024-06-15 2024-06-15 108.0 T CI D \n", + "18 1335318 1757258011 2024-06-13 2024-06-13 109.0 T CI D \n", + "19 1335319 1757258011 2024-06-15 2024-06-15 106.0 T CI D \n", + "\n", + " description d4 \n", + "0 CBN Stamp Duty Charge 1945204 \n", + "1 MTHLY ALLO MAR 2018. 1945204 \n", + "2 WELFARE MAR. 2018 1945204 \n", + "3 Online : blessfyne 1945204 \n", + "4 ATM WD @ FCMB Trans-Amadi 2 ATM1PortHarcourt RVNG 1945204 \n", + "5 web:abdulai 2184939 \n", + "6 CSH DEPOSIT BY : ROTIMI OLUWATOBI 2184939 \n", + "7 MTHLY ALLO MAR 2018. 2184939 \n", + "8 CBN Stamp Duty Charge 2184939 \n", + "9 ATM WD @ FCMB ONIPANU-2 IKORODU RD, LAGOS LANG 2184939 \n", + "10 368837107 BILL PAY @ 12141451/2184939014 2184939 \n", + "11 ATM WD @ FCMB ONIPANU-3 IKORODU RD, LAGOS LANG 2184939 \n", + "12 ATM WD @ FCMB ONIPANU-3 IKORODU RD, LAGOS LANG 2184939 \n", + "13 QR/Q10009078250/Airtime Top-up/2347037279970 1757258 \n", + "14 1019323435/WEB PURCHASE @ 3IWPDNRB/ww.nairabet... 1757258 \n", + "15 QR/Q10009079949/Airtime Top-up/2347037279970 1757258 \n", + "16 QR/Q10009036640/Airtime Top-up/2347037279970 1757258 \n", + "17 QR/Q10008928860/Airtime Top-up/2347037279970 1757258 \n", + "18 1013759550/WEB PURCHASE @ 3IWPDNRB/ww.nairabet... 1757258 \n", + "19 QR/Q10008860487/Airtime Top-up/2347037279970 1757258 " + ], + "text/html": [ + "\n", + "
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Salary Transactions Identification Heuristics\n", + "**Heuristic Design Principles**\n", + "\n", + "**Meta Criteria:** Salary should be a credit, denoted as `\"C\"` in the `d3` column.\n", + "1. Mention of salary in the description. **Keywords**:\"salary\" \"payroll\" \"income\"\n", + "2. Investigate patterns in the confirmed salary transactions.\n", + "\n", + " **a. Time Period:** Significant difference in money received in \"salary_periods\" and remainder of the months.\n", + "\n", + " **b. Transaction types:** See if there are patterns in transaction type for verified salary transactions agianst others.\n", + "\n", + "3. Consistently recieve an outlier(large) but similar credit amount of money every month.\n", + "\n" + ], + "metadata": { + "id": "HMsJe_x6qEjK" + } + }, + { + "cell_type": "markdown", + "source": [ + "### H1: Salary in Description" + ], + "metadata": { + "id": "J2yi-nDm2TJJ" + } + }, + { + "cell_type": "code", + "source": [ + "# look for transactions with salary in the description\n", + "df[\"H1\"] =0\n", + "\n", + "df.loc[((df[\"description\"].str.contains(\"salary\", case=False)) & (df[\"d3\"]== 'C')), \"H1\"] = 1\n", + "\n", + "salary_description_transactions = df[df[\"H1\"]==1]\n", + "\n", + "display(salary_description_transactions.head(20))\n", + "\n", + "print(f\"Identified {len(salary_description_transactions)} salary-like transactions\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 766 + }, + "id": "knQcLweriJEQ", + "outputId": "2a651385-7e60-44aa-f692-a4baf1032544" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + " id accountid trx_start_date trx_end_date amount d1 d2 d3 \\\n", + "1434 1336731 2083803018 2024-06-11 2024-06-11 39844.3416 T CI C \n", + "3939 1525237 2241150015 2024-06-14 2024-06-14 45900.0000 T BI C \n", + "6195 1341485 1411215013 2024-06-15 2024-06-15 111116.0052 T CI C \n", + "8416 1343700 1269970016 2024-06-16 2024-06-16 43261.0632 T CI C \n", + "10151 1345432 1947900010 2024-06-15 2024-06-15 9048.0000 T CI C \n", + "10831 1346111 1218765016 2024-06-12 2024-06-12 40457.1054 T CI C \n", + "10977 1346257 1275044015 2024-06-13 2024-06-13 135318.8824 T CI C \n", + "11764 1347043 2253348011 2024-06-14 2024-06-14 25721.5286 T CI C \n", + "11805 1347084 1957221017 2024-06-12 2024-06-12 11391.1928 T CI C \n", + "12956 1348237 1155907018 2024-06-12 2024-06-12 81146.8245 T CI C \n", + "14929 1350209 1543039017 2024-06-07 2024-06-07 47321.8595 T I C \n", + "15470 1350750 2131439013 2024-06-12 2024-06-12 118615.8665 T CI C \n", + "15473 1350753 2131439013 2024-06-14 2024-06-14 115290.1880 T CI C \n", + "15543 1350823 1516029010 2024-06-16 2024-06-16 146963.9806 T CI C \n", + "16794 1352075 1989415019 2024-06-14 2024-06-14 126280.1904 T CI C \n", + "17771 1353052 1998110015 2024-06-12 2024-06-12 13851.0000 T CI C \n", + "18041 1353322 1259062019 2024-06-10 2024-06-10 27510.5322 T CI C \n", + "19668 1354949 2020313017 2024-06-10 2024-06-10 5500.0000 T CI C \n", + "22009 1357281 2117066019 2024-06-13 2024-06-13 42059.3670 T CI C \n", + "25213 1360480 0897678011 2024-06-10 2024-06-10 55141.2663 T CI C \n", + "\n", + " description d4 year \\\n", + "1434 IMSG MARCH SALARY 2083803 2024 \n", + "3939 SALARY PAYMENTS 2241150 2024 \n", + "6195 IMSG MARCH SALARY 1411215 2024 \n", + "8416 IMSG MARCH 2018 SALARY 1269970 2024 \n", + "10151 SALARY MAR. 2018 1947900 2024 \n", + "10831 IMSG MARCH 2018 SALARY 1218765 2024 \n", + "10977 106/MAR2018 Salary/3152861/ LANG 1275044 2024 \n", + "11764 N-10022682239/OYIGBO LOC-FEB 2018 SALARY - OYI 2253348 2024 \n", + "11805 MARCH 2018 SALARY BAPTIST HIGH SCH 11 1957221 2024 \n", + "12956 IMSG MARCH SALARY 1155907 2024 \n", + "14929 FEB SALARY=DESOPADEC 1543039 2024 \n", + "15470 R-215105337/UNIVE:JANUARY2018NETSALARY:CBN:215 2131439 2024 \n", + "15473 R-215063626/UNIVE:DECEMBER2017NETSALARY:CBN:21 2131439 2024 \n", + "15543 N-10022787984/MAR 18 Salary:CITI:: 1516029 2024 \n", + "16794 R-214329984/TRANS:SALARY:CBN:88829762-65070-:2 1989415 2024 \n", + "17771 STAFF SALARY MAR 2018 1998110 2024 \n", + "18041 COP08DPSS960249;54469074;NSUK FEB 2018 SALARY-NSU 1259062 2024 \n", + "19668 web:Upfront salary payment 2020313 2024 \n", + "22009 IMSG MARCH 2018 SALARY 2117066 2024 \n", + "25213 IMSG MARCH SALARY 0897678 2024 \n", + "\n", + " month day_of_month H1 \n", + "1434 6 11 1 \n", + "3939 6 14 1 \n", + "6195 6 15 1 \n", + "8416 6 16 1 \n", + "10151 6 15 1 \n", + "10831 6 12 1 \n", + "10977 6 13 1 \n", + "11764 6 14 1 \n", + "11805 6 12 1 \n", + "12956 6 12 1 \n", + "14929 6 7 1 \n", + "15470 6 12 1 \n", + "15473 6 14 1 \n", + "15543 6 16 1 \n", + "16794 6 14 1 \n", + "17771 6 12 1 \n", + "18041 6 10 1 \n", + "19668 6 10 1 \n", + "22009 6 13 1 \n", + "25213 6 10 1 " + ], + "text/html": [ + "\n", + "
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idaccountidtrx_start_datetrx_end_dateamountd1d2d3descriptiond4yearmonthday_of_monthH1
1434133673120838030182024-06-112024-06-1139844.3416TCICIMSG MARCH SALARY208380320246111
3939152523722411500152024-06-142024-06-1445900.0000TBICSALARY PAYMENTS224115020246141
6195134148514112150132024-06-152024-06-15111116.0052TCICIMSG MARCH SALARY141121520246151
8416134370012699700162024-06-162024-06-1643261.0632TCICIMSG MARCH 2018 SALARY126997020246161
10151134543219479000102024-06-152024-06-159048.0000TCICSALARY MAR. 2018194790020246151
10831134611112187650162024-06-122024-06-1240457.1054TCICIMSG MARCH 2018 SALARY121876520246121
10977134625712750440152024-06-132024-06-13135318.8824TCIC106/MAR2018 Salary/3152861/ LANG127504420246131
11764134704322533480112024-06-142024-06-1425721.5286TCICN-10022682239/OYIGBO LOC-FEB 2018 SALARY - OYI225334820246141
11805134708419572210172024-06-122024-06-1211391.1928TCICMARCH 2018 SALARY BAPTIST HIGH SCH 11195722120246121
12956134823711559070182024-06-122024-06-1281146.8245TCICIMSG MARCH SALARY115590720246121
14929135020915430390172024-06-072024-06-0747321.8595TICFEB SALARY=DESOPADEC15430392024671
15470135075021314390132024-06-122024-06-12118615.8665TCICR-215105337/UNIVE:JANUARY2018NETSALARY:CBN:215213143920246121
15473135075321314390132024-06-142024-06-14115290.1880TCICR-215063626/UNIVE:DECEMBER2017NETSALARY:CBN:21213143920246141
15543135082315160290102024-06-162024-06-16146963.9806TCICN-10022787984/MAR 18 Salary:CITI::151602920246161
16794135207519894150192024-06-142024-06-14126280.1904TCICR-214329984/TRANS:SALARY:CBN:88829762-65070-:2198941520246141
17771135305219981100152024-06-122024-06-1213851.0000TCICSTAFF SALARY MAR 2018199811020246121
18041135332212590620192024-06-102024-06-1027510.5322TCICCOP08DPSS960249;54469074;NSUK FEB 2018 SALARY-NSU125906220246101
19668135494920203130172024-06-102024-06-105500.0000TCICweb:Upfront salary payment202031320246101
22009135728121170660192024-06-132024-06-1342059.3670TCICIMSG MARCH 2018 SALARY211706620246131
25213136048008976780112024-06-102024-06-1055141.2663TCICIMSG MARCH SALARY089767820246101
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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "summary": "{\n \"name\": \"print(f\\\"Identified {len(salary_description_transactions)} salary-like transactions\\\")\",\n \"rows\": 20,\n \"fields\": [\n {\n \"column\": \"id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 39725,\n \"min\": 1336731,\n \"max\": 1525237,\n \"num_unique_values\": 20,\n \"samples\": [\n 1336731,\n 1354949,\n 1353052\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"accountid\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 19,\n \"samples\": [\n \"2083803018\",\n \"1218765016\",\n \"2131439013\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"trx_start_date\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2024-06-07 00:00:00\",\n \"max\": \"2024-06-16 00:00:00\",\n \"num_unique_values\": 8,\n \"samples\": [\n \"2024-06-14 00:00:00\",\n \"2024-06-13 00:00:00\",\n \"2024-06-11 00:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"trx_end_date\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2024-06-07 00:00:00\",\n \"max\": \"2024-06-16 00:00:00\",\n \"num_unique_values\": 8,\n \"samples\": [\n \"2024-06-14 00:00:00\",\n \"2024-06-13 00:00:00\",\n \"2024-06-11 00:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"amount\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 46542.19951227044,\n \"min\": 5500.0,\n \"max\": 146963.9806,\n \"num_unique_values\": 20,\n \"samples\": [\n 39844.3416,\n 5500.0,\n 13851.000000000002\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"d1\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"T\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"d2\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"CI\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"d3\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"C\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"description\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 15,\n \"samples\": [\n \"R-215063626/UNIVE:DECEMBER2017NETSALARY:CBN:21\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"d4\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 19,\n \"samples\": [\n \"2083803\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"year\",\n \"properties\": {\n \"dtype\": \"int32\",\n \"num_unique_values\": 1,\n \"samples\": [\n 2024\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"month\",\n \"properties\": {\n \"dtype\": \"int32\",\n \"num_unique_values\": 1,\n \"samples\": [\n 6\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"day_of_month\",\n \"properties\": {\n \"dtype\": \"int32\",\n \"num_unique_values\": 8,\n \"samples\": [\n 14\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"H1\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 1,\n \"max\": 1,\n \"num_unique_values\": 1,\n \"samples\": [\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Identified 4228 salary-like transactions\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "NB: Some accounts receive multiple salaries in the same month. e.g accountid, \"0551610021\" which received 28 salary described transactions within the space of 12 months. The amounts were not always the same, meaning it wasn't duplicate/bounced transactions, it was infact multi-salaries. Current fix: Use max salary received in a month, to get the monthly received salary for the month.\n", + "Alternative: Sum all salries in a month." + ], + "metadata": { + "id": "_6UsJNKUaGZL" + } + }, + { + "cell_type": "markdown", + "source": [ + "### H2: Investigate patterns in transaction types and dates for known salary transactions." + ], + "metadata": { + "id": "Jw4vszdwdc78" + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Days of Month" + ], + "metadata": { + "id": "BT7H2p8p4gLr" + } + }, + { + "cell_type": "code", + "source": [ + "# Calculate and display frequency of salary payments by day of month\n", + "print(\"Salary Payment Day Frequency:\")\n", + "print(salary_description_transactions.day_of_month.value_counts())\n", + "\n", + "# Create histogram to visualize distribution of payment days\n", + "ax = salary_description_transactions.day_of_month.plot(\n", + " kind=\"hist\",\n", + " bins=31,\n", + " edgecolor='black',\n", + " figsize=(10, 6),\n", + " title=\"Distribution of Salary Payment Days\\n(Frequency by Day of Month)\",\n", + " xlabel=\"Day of Month\",\n", + " ylabel=\"Number of Salary Payments\",\n", + " color='#1f77b4'\n", + ")\n", + "\n", + "# Add grid for better readability\n", + "ax.grid(axis='y', alpha=0.3)\n", + "\n", + "\n", + "# Calculate percentage of salaries paid in top 7 most common days\n", + "top_days_coverage = (\n", + " salary_description_transactions.day_of_month\n", + " .value_counts()\n", + " .nlargest(7)\n", + " .sum() / salary_description_transactions.shape[0]\n", + ") * 100 # Convert to percentage\n", + "\n", + "print(f\"\\nTop 7 payment days account for {top_days_coverage:.1f}% of all salary transactions\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "3zTsFcAmn8RU", + "outputId": "ac4bc872-1bdc-4668-d0e7-25ec2a1dcc73" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Salary Payment Day Frequency:\n", + "day_of_month\n", + "20 226\n", + "21 206\n", + "19 194\n", + "18 193\n", + "22 180\n", + "17 174\n", + "23 163\n", + "16 146\n", + "25 146\n", + "24 144\n", + "15 142\n", + "13 135\n", + "12 133\n", + "29 133\n", + "11 129\n", + "28 126\n", + "2 126\n", + "14 125\n", + "6 125\n", + "3 123\n", + "30 121\n", + "7 119\n", + "27 115\n", + "10 112\n", + "5 112\n", + "1 111\n", + "9 108\n", + "26 107\n", + "4 105\n", + "8 97\n", + "31 52\n", + "Name: count, dtype: int64\n", + "\n", + "Top 7 payment days account for 31.6% of all salary transactions\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### Transaction Types" + ], + "metadata": { + "id": "5IoiWOIH4lbU" + } + }, + { + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# for all transaction type columns, d1-d3, print difference in proportion between confirmed salaries and unconfirmed.\n", + "def calculate_proportion_differences(column):\n", + " # Calculate proportions for salary vs non-salary\n", + " props = df.groupby('H1')[column].value_counts(normalize=True).unstack().fillna(0)\n", + "\n", + " # Calculate absolute percentage point differences\n", + " prop_diff = (props.loc[1] - props.loc[0]).abs().sort_values(ascending=False)\n", + "\n", + " # Format results\n", + " results = pd.concat([\n", + " props.loc[1].rename('Salary%'),\n", + " props.loc[0].rename('Non-Salary%'),\n", + " prop_diff.rename('Difference (pp)')\n", + " ], axis=1)\n", + "\n", + " return results.sort_values('Difference (pp)', ascending=False)\n", + "\n", + "# Analyze all transaction type columns\n", + "transaction_cols = ['d1', 'd2', 'd3']\n", + "\n", + "\n", + "def plot_component_bars(results_df, col, top_n=10):\n", + " # Filter top N categories with largest differences\n", + " top_categories = results_df.head(top_n).index\n", + "\n", + " # Get proportions for these categories\n", + " salary_props = results_df.loc[top_categories, 'Salary%']\n", + " non_salary_props = results_df.loc[top_categories, 'Non-Salary%']\n", + "\n", + " # Create figure\n", + " fig, ax = plt.subplots(figsize=(12, 6))\n", + "\n", + " # Set positions and width\n", + " x = np.arange(len(top_categories))\n", + " width = 0.35\n", + "\n", + " # Plot bars\n", + " rects1 = ax.bar(x - width/2, salary_props*100, width,\n", + " label='Salary Transactions', color='#1f77b4')\n", + " rects2 = ax.bar(x + width/2, non_salary_props*100, width,\n", + " label='Non-Salary Transactions', color='#ff7f0e')\n", + "\n", + " # Add labels and title\n", + " ax.set_ylabel('Percentage (%)')\n", + " ax.set_title(f'Transaction Type Composition: {col}')\n", + " ax.set_xticks(x)\n", + " ax.set_xticklabels(top_categories, rotation=45, ha='right')\n", + " ax.legend()\n", + "\n", + " # Add value labels\n", + " def autolabel(rects):\n", + " for rect in rects:\n", + " height = rect.get_height()\n", + " ax.annotate(f'{height:.1f}%',\n", + " xy=(rect.get_x() + rect.get_width() / 2, height),\n", + " xytext=(0, 3), # 3 points vertical offset\n", + " textcoords=\"offset points\",\n", + " ha='center', va='bottom', fontsize=8)\n", + "\n", + " autolabel(rects1)\n", + " autolabel(rects2)\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "# Analyze all transaction type columns\n", + "transaction_cols = ['d1', 'd2', 'd3']\n", + "\n", + "for col in transaction_cols:\n", + " print(f\"\\n\\033[1mTransaction Type Analysis: {col}\\033[0m\")\n", + " print(f\"Categories in salary transactions: {df[df['H1']==1][col].nunique()}\")\n", + "\n", + " diff_df = calculate_proportion_differences(col)\n", + " display(diff_df.head(10))\n", + "\n", + " # Plot component bar chart\n", + " plot_component_bars(diff_df, col, top_n=10)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "h20OR7Moz-XB", + "outputId": "2a1f35dd-e294-4968-8278-c5e25525f165" + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\u001b[1mTransaction Type Analysis: d1\u001b[0m\n", + "Categories in salary transactions: 1\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " Salary% Non-Salary% Difference (pp)\n", + "d1 \n", + "T 1.0 0.989452 0.010548\n", + "C 0.0 0.010221 0.010221\n", + "L 0.0 0.000327 0.000327" + ], + "text/html": [ + "\n", + "
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Salary%Non-Salary%Difference (pp)
d2
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SI0.0000000.0000440.000044
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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Conclusion**:\n", + "There are no clear distinguishing criterion for salaries using the days of the month and transaction types." + ], + "metadata": { + "id": "GGFCb__L4tbk" + } + }, + { + "cell_type": "markdown", + "source": [ + "### H3: Consistently high credits" + ], + "metadata": { + "id": "qm1FX4WyDR2-" + } + }, + { + "cell_type": "code", + "source": [ + "def identify_consistent_high_credits(df, n_months=3, cv_threshold=0.15):\n", + " \"\"\"\n", + " Identifies salary-like transactions by:\n", + " 1. Finding monthly max credits per account\n", + " 2. Calculating consistency (CV) of these max amounts\n", + " 3. Flagging ONLY the max transactions from consistent accounts\n", + " \"\"\"\n", + " # Convert to datetime and extract month\n", + " df['month'] = pd.to_datetime(df['trx_start_date']).dt.to_period('M')\n", + "\n", + " # Filter only credit transactions\n", + " credits = df[df['d3'] == 'C'].copy()\n", + "\n", + " # Get monthly max credits per account (with transaction IDs)\n", + " monthly_max = credits.loc[credits.groupby(['accountid', 'month'])['amount'].idxmax()]\n", + "\n", + " # Calculate dispersion metrics for each account\n", + " dispersion = monthly_max.groupby('accountid').agg(\n", + " mean_amount=('amount', 'mean'),\n", + " std_amount=('amount', 'std'),\n", + " count_months=('amount', 'count'),\n", + " mad=('amount', lambda x: np.mean(np.abs(x - np.mean(x)))), # Mean Absolute Deviation\n", + " cv=('amount', lambda x: np.mean(np.abs(x - np.mean(x)))/np.mean(x) if len(x) > 1 and np.mean(x) != 0 else np.nan) # MAD/Mean ratio as CV\n", + " ).reset_index()\n", + "\n", + " # Identify consistent accounts\n", + " consistent_accounts = dispersion[\n", + " (dispersion['count_months'] >= n_months) &\n", + " (dispersion['cv'] <= cv_threshold)\n", + " ]['accountid']\n", + "\n", + " # Create flag column (initialize with 0)\n", + " df['H3'] = 0\n", + "\n", + " # Flag ONLY the specific max transactions from consistent accounts\n", + " df.loc[\n", + " df.index.isin(monthly_max[monthly_max['accountid'].isin(consistent_accounts)].index),\n", + " 'H3'\n", + " ] = 1\n", + "\n", + " return df\n", + "\n", + "df = identify_consistent_high_credits(df, n_months=9, cv_threshold=0.15)\n", + "\n", + "# View flagged transactions\n", + "salary_candidates = df[df['H3'] == 1]\n", + "print(f\"Identified {len(salary_candidates)} salary-like transactions\")\n", + "display(salary_candidates.sort_values(['accountid', 'trx_start_date']))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 894 + }, + "id": "oGI8Esgez9p0", + "outputId": "84cb5be9-4e25-4fcc-f742-eea291cea4bc" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Identified 468635 salary-like transactions\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " id accountid trx_start_date trx_end_date amount d1 d2 d3 \\\n", + "4396016 376992 0006538016 2024-03-15 2024-03-15 10300.0 T CI C \n", + "4778410 759398 0006538016 2024-04-11 2024-04-11 10600.0 T CI C \n", + "5160885 1141852 0006538016 2024-05-14 2024-05-14 10700.0 T CI C \n", + "189013 1524306 0006538016 2024-06-10 2024-06-10 11000.0 T CI C \n", + "571487 1906760 0006538016 2024-07-04 2024-07-04 10700.0 T CI C \n", + "... ... ... ... ... ... .. .. .. \n", + "1913542 3248825 2277851016 2024-11-01 2024-11-01 8800.0 T CI C \n", + "2678451 4013733 2277851016 2024-12-29 2024-12-29 8000.0 T CI C \n", + "3060877 4396187 2277851016 2025-01-24 2025-01-24 8480.0 T CI C \n", + "3443339 4778641 2277851016 2025-02-22 2025-02-22 8400.0 T CI C \n", + "3825785 5161095 2277851016 2025-03-22 2025-03-22 8720.0 T CI C \n", + "\n", + " description d4 year \\\n", + "4396016 Online : wayde 0006538 2024 \n", + "4778410 Online : wayde 0006538 2024 \n", + "5160885 Online : wayde 0006538 2024 \n", + "189013 Online : wayde 0006538 2024 \n", + "571487 Online : wayde 0006538 2024 \n", + "... ... ... ... \n", + "1913542 NIP FRM ANYAPA MUHAMMED YAKUBU-FT For self RA 2277851 2024 \n", + "2678451 NIP FRM ANYAPA MUHAMMED YAKUBU-FT For self RA 2277851 2024 \n", + "3060877 NIP FRM ANYAPA MUHAMMED YAKUBU-FT For self RA 2277851 2025 \n", + "3443339 NIP FRM ANYAPA MUHAMMED YAKUBU-FT For self RA 2277851 2025 \n", + "3825785 NIP FRM ANYAPA MUHAMMED YAKUBU-FT For self RA 2277851 2025 \n", + "\n", + " month day_of_month H1 H3 \n", + "4396016 2024-03 15 0 1 \n", + "4778410 2024-04 11 0 1 \n", + "5160885 2024-05 14 0 1 \n", + "189013 2024-06 10 0 1 \n", + "571487 2024-07 4 0 1 \n", + "... ... ... .. .. \n", + "1913542 2024-11 1 0 1 \n", + "2678451 2024-12 29 0 1 \n", + "3060877 2025-01 24 0 1 \n", + "3443339 2025-02 22 0 1 \n", + "3825785 2025-03 22 0 1 \n", + "\n", + "[468635 rows x 15 columns]" + ], + "text/html": [ + "\n", + "
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idaccountidtrx_start_datetrx_end_dateamountd1d2d3descriptiond4yearmonthday_of_monthH1H3
439601637699200065380162024-03-152024-03-1510300.0TCICOnline : wayde000653820242024-031501
477841075939800065380162024-04-112024-04-1110600.0TCICOnline : wayde000653820242024-041101
5160885114185200065380162024-05-142024-05-1410700.0TCICOnline : wayde000653820242024-051401
189013152430600065380162024-06-102024-06-1011000.0TCICOnline : wayde000653820242024-061001
571487190676000065380162024-07-042024-07-0410700.0TCICOnline : wayde000653820242024-07401
................................................
1913542324882522778510162024-11-012024-11-018800.0TCICNIP FRM ANYAPA MUHAMMED YAKUBU-FT For self RA227785120242024-11101
2678451401373322778510162024-12-292024-12-298000.0TCICNIP FRM ANYAPA MUHAMMED YAKUBU-FT For self RA227785120242024-122901
3060877439618722778510162025-01-242025-01-248480.0TCICNIP FRM ANYAPA MUHAMMED YAKUBU-FT For self RA227785120252025-012401
3443339477864122778510162025-02-222025-02-228400.0TCICNIP FRM ANYAPA MUHAMMED YAKUBU-FT For self RA227785120252025-022201
3825785516109522778510162025-03-222025-03-228720.0TCICNIP FRM ANYAPA MUHAMMED YAKUBU-FT For self RA227785120252025-032201
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Extract month period and filter salary transactions\n", + " df['year_month'] = pd.to_datetime(df['trx_start_date']).dt.to_period('M')\n", + " # Extract day of month from transaction date\n", + " df['day_of_month'] = pd.to_datetime(df['trx_start_date']).dt.day\n", + "\n", + " salary_txns = df[df[col] == 1].copy()\n", + "\n", + " # 2. Identify salary earners (accounts with salary in >= N months)\n", + " salary_months = salary_txns.groupby('accountid')['year_month'].nunique()\n", + " salary_earners = salary_months[salary_months >= min_salary_months].index\n", + "\n", + " # 3. Get transactions only for salary earners\n", + " salary_earner_txns = salary_txns[salary_txns['accountid'].isin(salary_earners)]\n", + "\n", + " # 4. For each account and month, find the transaction with the maximum amount\n", + " # This assumes the largest transaction is the main salary\n", + " idx_max_amount = salary_earner_txns.groupby(['accountid', 'year_month'])['amount'].idxmax()\n", + "\n", + " # 5. Create summary with the day of month information\n", + " max_txns = salary_earner_txns.loc[idx_max_amount]\n", + "\n", + " # 6. Aggregate and create the summary dataframe\n", + " summary = (\n", + " max_txns\n", + " .groupby(['accountid', 'year_month'])\n", + " .agg(\n", + " salary_max=('amount', 'max'),\n", + " day_of_month=('day_of_month', 'first') # Get day of month from the max transaction\n", + " )\n", + " .reset_index()\n", + " )\n", + "\n", + " # 7. Add month sequence for trend analysis\n", + " summary['month_seq'] = (\n", + " summary.groupby('accountid')['year_month']\n", + " .rank(method='dense').astype(int)\n", + " )\n", + "\n", + " return summary.sort_values(['accountid', 'year_month'])\n", + "\n", + "# Generate the summary for verified salary transactions (using H1)\n", + "verified_salary_earner_summary = get_salary_earner_summary(df, min_salary_months=9)\n", + "\n", + "# Display results\n", + "print(f\"Found {verified_salary_earner_summary['accountid'].nunique()} verified salary earners\")\n", + "display(verified_salary_earner_summary.head(20))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 694 + }, + "id": "PWVY8QLqu4ud", + "outputId": "0e617d94-8955-4ff9-867b-f4a4ccbedf03" + }, + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Found 290 verified salary earners\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " accountid year_month salary_max day_of_month month_seq\n", + "0 0532207024 2024-03 59950.0 23 1\n", + "1 0532207024 2024-04 58850.0 16 2\n", + "2 0532207024 2024-05 57200.0 15 3\n", + "3 0532207024 2024-06 56650.0 16 4\n", + "4 0532207024 2024-07 56650.0 9 5\n", + "5 0532207024 2024-08 60500.0 10 6\n", + "6 0532207024 2024-09 57750.0 6 7\n", + "7 0532207024 2024-10 59950.0 30 8\n", + "8 0532207024 2024-11 56100.0 26 9\n", + "9 0532207024 2024-12 58300.0 24 10\n", + "10 0532207024 2025-01 59950.0 22 11\n", + "11 0532207024 2025-02 57750.0 20 12\n", + "12 0532207024 2025-03 56100.0 22 13\n", + "13 0532452020 2024-03 83200.0 21 1\n", + "14 0532452020 2024-04 82400.0 16 2\n", + "15 0532452020 2024-05 81600.0 14 3\n", + "16 0532452020 2024-06 84000.0 12 4\n", + "17 0532452020 2024-07 83200.0 12 5\n", + "18 0532452020 2024-08 86400.0 11 6\n", + "19 0532452020 2024-09 80000.0 7 7" + ], + "text/html": [ + "\n", + "
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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "summary": "{\n \"name\": \"display(verified_salary_earner_summary\",\n \"rows\": 20,\n \"fields\": [\n {\n \"column\": \"accountid\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"0532452020\",\n \"0532207024\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"year_month\",\n \"properties\": {\n \"dtype\": \"period[M]\",\n \"num_unique_values\": 13,\n \"samples\": [\n \"2025-02\",\n \"2024-12\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"salary_max\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 12272.021023450048,\n \"min\": 56100.0,\n \"max\": 86400.0,\n \"num_unique_values\": 14,\n \"samples\": [\n 82400.0,\n 84000.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"day_of_month\",\n \"properties\": {\n \"dtype\": \"int32\",\n \"num_unique_values\": 16,\n \"samples\": [\n 23,\n 16\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"month_seq\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3,\n \"min\": 1,\n \"max\": 13,\n \"num_unique_values\": 13,\n \"samples\": [\n 12,\n 10\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# extract for probable salary earners\n", + "\n", + "# exclude already verified salary eaners\n", + "unverified_df = df[~df[\"accountid\"].isin(verified_salary_earner_summary[\"accountid\"].unique())]\n", + "\n", + "probable_salary_earner_summary = get_salary_earner_summary(unverified_df, min_salary_months=9, col = \"H3\")\n", + "\n", + "# Display results\n", + "print(f\"Found {probable_salary_earner_summary['accountid'].nunique()} probable salary earners\")\n", + "display(probable_salary_earner_summary.head(20))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 694 + }, + "id": "XjlW_AQnn4Uh", + "outputId": "bb4041d0-e28a-494f-8a4b-8d656760ee02" + }, + "execution_count": 23, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Found 35867 probable salary earners\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " accountid year_month salary_max day_of_month month_seq\n", + "0 0006538016 2024-03 10300.0 15 1\n", + "1 0006538016 2024-04 10600.0 11 2\n", + "2 0006538016 2024-05 10700.0 14 3\n", + "3 0006538016 2024-06 11000.0 10 4\n", + "4 0006538016 2024-07 10700.0 4 5\n", + "5 0006538016 2024-08 10700.0 4 6\n", + "6 0006538016 2024-09 10700.0 2 7\n", + "7 0006538016 2024-10 10700.0 28 8\n", + "8 0006538016 2024-11 10400.0 21 9\n", + "9 0006538016 2024-12 10000.0 19 10\n", + "10 0006538016 2025-01 10100.0 18 11\n", + "11 0006538016 2025-02 10200.0 18 12\n", + "12 0006538016 2025-03 10900.0 14 13\n", + "13 0010954040 2024-03 74900.0 25 1\n", + "14 0010954040 2024-04 74200.0 18 2\n", + "15 0010954040 2024-05 72100.0 16 3\n", + "16 0010954040 2024-06 72800.0 14 4\n", + "17 0010954040 2024-07 74200.0 10 5\n", + "18 0010954040 2024-08 72100.0 12 6\n", + "19 0010954040 2024-09 74900.0 6 7" + ], + "text/html": [ + "\n", + "
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1500109540402024-0572100.0163
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summary_table: DataFrame from get_salary_earner_summary function\n", + "\n", + " Returns:\n", + " - DataFrame with columns: [accountid, avg_monthly_salary, next_salary_amount, next_salary_date]\n", + " \"\"\"\n", + "\n", + "\n", + " # Group by accountid to get account-level statistics\n", + " account_predictions = []\n", + "\n", + " for account_id, account_data in summary_table.groupby('accountid'):\n", + " # Sort data by year_month in descending order\n", + " account_data = account_data.sort_values('year_month', ascending=False)\n", + "\n", + " # Calculate overall median salary (all available months)\n", + " median_salary_all = account_data['salary_max'].median()\n", + "\n", + " # Get last 6 months of data or all if less than 6 months available\n", + " last_six_months = account_data.head(min(6, len(account_data)))\n", + "\n", + " # Calculate median salary and day of month from last 6 months\n", + " median_salary_recent = last_six_months['salary_max'].median()\n", + " median_day = int(last_six_months['day_of_month'].median())\n", + "\n", + " # Get the last known payment information\n", + " last_payment = account_data.iloc[0]\n", + "\n", + " # Convert Period to timestamp (get first day of month)\n", + " # This is safer than direct conversion which might raise errors\n", + " last_month_str = str(last_payment['year_month'])\n", + " last_month = pd.to_datetime(last_month_str)\n", + "\n", + " # Predict next month\n", + " if last_month.month == 12:\n", + " next_month_year = last_month.year + 1\n", + " next_month_month = 1\n", + " else:\n", + " next_month_year = last_month.year\n", + " next_month_month = last_month.month + 1\n", + "\n", + " # Get the number of days in the next month\n", + " next_month_days = calendar.monthrange(next_month_year, next_month_month)[1]\n", + "\n", + " # Adjust day if necessary\n", + " adjusted_day = min(median_day, next_month_days)\n", + "\n", + " # Create the predicted date\n", + " predicted_date = pd.Timestamp(year=next_month_year,\n", + " month=next_month_month,\n", + " day=adjusted_day)\n", + "\n", + " # Append to results\n", + " account_predictions.append({\n", + " 'accountid': account_id,\n", + " 'avg_monthly_salary': round(median_salary_all, 2),\n", + " 'next_salary_amount': round(median_salary_recent, 2),\n", + " 'next_salary_date': predicted_date.strftime('%Y-%m-%d')\n", + " })\n", + "\n", + " # Convert to DataFrame\n", + " prediction_table = pd.DataFrame(account_predictions)\n", + "\n", + " return prediction_table\n", + "\n", + "prediction_table = predict_next_salary(salary_earner_summary)\n", + "prediction_table.head(20)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 676 + }, + "id": "-xlLGZYGvO2w", + "outputId": "772724f7-4ac5-46e5-db3d-95185580d3c5" + }, + "execution_count": 30, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " accountid avg_monthly_salary next_salary_amount next_salary_date\n", + "0 0006538016 10700.0 10300.0 2025-04-18\n", + "1 0010954040 74200.0 74200.0 2025-04-21\n", + "2 0020801031 160500.0 162000.0 2025-04-23\n", + "3 0022324028 41200.0 41200.0 2025-04-23\n", + "4 0022848049 1463760.0 1477440.0 2025-04-22\n", + "5 0025339021 586360.0 589100.0 2025-04-23\n", + "6 0027643012 106000.0 108000.0 2025-04-23\n", + "7 0028024021 515000.0 517500.0 2025-04-20\n", + "8 0034199027 140450.0 141775.0 2025-04-19\n", + "9 0035106020 2180.0 2190.0 2025-04-20\n", + "10 0046472024 14700.0 14560.0 2025-04-25\n", + "11 0050282020 15750.0 15900.0 2025-04-20\n", + "12 0051390023 159000.0 161250.0 2025-04-20\n", + "13 0051987023 37100.0 37100.0 2025-04-18\n", + "14 0053388019 145080.0 144382.5 2025-04-17\n", + "15 0060225026 156000.0 159750.0 2025-04-25\n", + "16 0060968040 208.0 214.0 2025-04-21\n", + "17 0068203024 155040.0 158840.0 2025-04-22\n", + "18 0103261028 95680.0 95220.0 2025-04-23\n", + "19 0104926023 42800.0 42000.0 2025-04-12" + ], + "text/html": [ + "\n", + "
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accountidavg_monthly_salarynext_salary_amountnext_salary_date
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1001095404074200.074200.02025-04-21
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150060225026156000.0159750.02025-04-25
160060968040208.0214.02025-04-21
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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "prediction_table", + "summary": "{\n \"name\": \"prediction_table\",\n \"rows\": 36157,\n \"fields\": [\n {\n \"column\": \"accountid\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 36157,\n \"samples\": [\n \"1539827015\",\n \"2185975011\",\n \"1980514012\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"avg_monthly_salary\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 633779.5101455783,\n \"min\": 0.19,\n \"max\": 61285000.0,\n \"num_unique_values\": 6916,\n \"samples\": [\n 20790.0,\n 293.08,\n 73736.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"next_salary_amount\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 642057.2400670127,\n \"min\": 0.19,\n \"max\": 63962500.0,\n \"num_unique_values\": 8495,\n \"samples\": [\n 40000.0,\n 10506.0,\n 79875.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"next_salary_date\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 21,\n \"samples\": [\n \"2025-04-18\",\n \"2025-04-27\",\n \"2025-04-15\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 30 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# get number of customers with at least 10k predicted salary\n", + "high_earning_customers = prediction_table[prediction_table[\"avg_monthly_salary\"] >= 10000]\n", + "\n", + "print(f\"Number of customers with at least 10k predicted salary: {high_earning_customers.shape[0]}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PaZLRoIVySB9", + "outputId": "5f4fab25-047a-429d-d99c-d83580f291f5" + }, + "execution_count": 31, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Number of customers with at least 10k predicted salary: 23978\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# write table to csv\n", + "prediction_table.to_csv(\"salary_earner_prediction.csv\", index=False)" + ], + "metadata": { + "id": "xE-vTMY9Atmq" + }, + "execution_count": 32, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# 4. ML Model Prediction" + ], + "metadata": { + "id": "qSiu_T3X7PYr" + } + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "# Filter accounts with at least 12 months data\n", + "valid_accounts = salary_earner_summary.groupby('accountid').filter(lambda x: x['month_seq'].max() >= 12)\n", + "\n", + "def create_safe_features(df):\n", + " df = df.copy().sort_values(['accountid', 'month_seq'])\n", + "\n", + " # Lag features (safe - only uses past data)\n", + " df['lag_1'] = df.groupby('accountid')['salary_max'].shift(1)\n", + " df['lag_2'] = df.groupby('accountid')['salary_max'].shift(2)\n", + " df['lag_3'] = df.groupby('accountid')['salary_max'].shift(3)\n", + "\n", + " # Expanding window statistics (safe - only uses past data)\n", + " df['expanding_mean'] = df.groupby('accountid')['salary_max'].expanding().mean().reset_index(0, drop=True)\n", + " df['expanding_std'] = df.groupby('accountid')['salary_max'].expanding().std().reset_index(0, drop=True)\n", + "\n", + " # Month-over-month change (safe - using lag 1)\n", + " df['mom_change'] = df.groupby('accountid')['salary_max'].pct_change()\n", + "\n", + " return df.dropna()\n", + "\n", + "safe_featured_data = create_safe_features(valid_accounts)\n", + "\n", + "# Split into train/test (first 10 vs last 2 months per account)\n", + "train_data = safe_featured_data[safe_featured_data['month_seq'] <= 10]\n", + "test_data = safe_featured_data[safe_featured_data['month_seq'] > 10]\n", + "\n", + "# Prepare features\n", + "safe_features = ['lag_1', 'lag_2', 'lag_3', 'expanding_mean', 'expanding_std', 'mom_change']\n", + "X_train = train_data[safe_features]\n", + "y_train = train_data['salary_max']\n", + "X_test = test_data[safe_features]\n", + "y_test = test_data['salary_max']\n", + "\n", + "# Scale features\n", + "scaler = StandardScaler()\n", + "X_train_scaled = scaler.fit_transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)" + ], + "metadata": { + "id": "wh4SEaJx79W_" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "\n", + "model = LinearRegression()\n", + "model.fit(X_train_scaled, y_train)\n", + "\n", + "#Evaluation\n", + "from sklearn.metrics import mean_absolute_error, r2_score\n", + "\n", + "# Make predictions\n", + "test_preds = model.predict(X_test_scaled)\n", + "\n", + "# Calculate metrics\n", + "test_mae = mean_absolute_error(y_test, test_preds)\n", + "\n", + "test_r2 = r2_score(y_test, test_preds)\n", + "\n", + "print(f\"Test MAE: {test_mae:.2f}\")\n", + "print(f\"Test R²: {test_r2:.3f}\")\n", + "\n", + "# Create results dataframe\n", + "results = test_data[['accountid', 'month_seq', 'salary_max']].copy()\n", + "results['predicted'] = test_preds" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-MOnchDEEJWd", + "outputId": "b5f85d12-f818-4405-8a00-501f380ba9ea" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Test MAE: 3537.68\n", + "Test R²: 0.996\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Randomly sample 100 points for clearer visualization\n", + "sample_results = results.sample(100)\n", + "\n", + "plt.figure(figsize=(8, 8))\n", + "sns.scatterplot(data=sample_results, x='salary_max', y='predicted', alpha=0.7)\n", + "\n", + "# Add perfect prediction line\n", + "max_val = max(sample_results['salary_max'].max(), sample_results['predicted'].max())\n", + "min_val = min(sample_results['salary_max'].min(), sample_results['predicted'].min())\n", + "plt.plot([min_val, max_val], [min_val, max_val], 'r--', label='Perfect Prediction')\n", + "\n", + "plt.xlabel(\"Actual Salary Amount\", fontsize=12)\n", + "plt.ylabel(\"Predicted Salary Amount\", fontsize=12)\n", + "plt.title(f\"Actual vs Predicted Salaries\\n(Random Sample of 100 Points)\", fontsize=14)\n", + "plt.legend()\n", + "plt.grid(True, alpha=0.3)\n", + "\n", + "# Add RMSE annotation\n", + "plt.annotate(f'Test MAE: {test_mae:.2f}',\n", + " xy=(0.05, 0.95),\n", + " xycoords='axes fraction',\n", + " fontsize=12,\n", + " bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))\n", + "\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 745 + }, + "id": "h1A0_QYrHhHa", + "outputId": "7b241810-fa35-4b5f-a489-708a3a12c797" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Pushing to Git from Colab" + ], + "metadata": { + "id": "7ijJTmw2EkYI" + } + }, + { + "cell_type": "code", + "source": [ + "# Pushing code\n", + "# First, install git and set up credentials\n", + "!apt-get install git -y\n", + "!git config --global user.name \"makindekayode75\"\n", + "!git config --global user.email \"makindekayode75@gmail.com\"" + ], + "metadata": { + "id": "Ko2zG1JKJfgJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "8862a5fd-0a68-478b-9ca2-2412cdf0ce18" + }, + "execution_count": 36, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Reading package lists... Done\n", + "Building dependency tree... Done\n", + "Reading state information... Done\n", + "git is already the newest version (1:2.34.1-1ubuntu1.12).\n", + "0 upgraded, 0 newly installed, 0 to remove and 29 not upgraded.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Clone the repository (replace with your credentials)\n", + "!git clone https://makindekayode75:MgK1nDe.F7f22!x@gitlab.chiefsoft.net/DigiFi/digifi-Anayltics.git\n", + "\n", + "# Navigate to the repository\n", + "%cd digifi-Anayltics/" + ], + "metadata": { + "id": "_ZiLmYIlENx8" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Copy your files from Colab to the local repository\n", + "\n", + "# Add, commit and push the files\n", + "!git add .\n", + "!git commit -m \"First commit from Colab\"\n", + "!git push origin master" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "RGKVthOWDX75", + "outputId": "1e94e2c4-b8b3-4dc1-9838-fea6908fce5e" + }, + "execution_count": 37, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[master (root-commit) d7de6ed] First commit from Colab\n", + " 3 files changed, 504787 insertions(+)\n", + " create mode 100644 probable_salary_earner_summary.csv\n", + " create mode 100644 salary_earner_prediction.csv\n", + " create mode 100644 verified_salary_earner_summary.csv\n", + "Enumerating objects: 5, done.\n", + "Counting objects: 100% (5/5), done.\n", + "Delta compression using up to 2 threads\n", + "Compressing objects: 100% (5/5), done.\n", + "Writing objects: 100% (5/5), 3.56 MiB | 1.37 MiB/s, done.\n", + "Total 5 (delta 0), reused 0 (delta 0), pack-reused 0\n", + "To https://gitlab.chiefsoft.net/DigiFi/digifi-Anayltics.git\n", + " * [new branch] master -> master\n" + ] + } + ] + } + ] +} \ No newline at end of file