lara1510/Savings_Plan_Generator
0
1{2 "cells": [3 {4 "cell_type": "markdown",5 "metadata": {6 "id": "mJ-glFHLrhOu"7 },8 "source": [9 "\n",10 "This notebook processes a dataset from Kaggle named 'Personal Transactions'. The dataset contains detailed records of personal financial transactions, including both credit and debit transactions. It includes categories such as shopping, rent payments, dining out, utility bills, and more.\n",11 "\n",12 "Dataset Link: https://www.kaggle.com/datasets/bukolafatunde/personal-finance/data?select=personal_transactions.csv\n",13 "\n",14 "The goal is to prepare the data for training a linear regression model. Specifically, we aim to analyze monthly spending patterns and use historical transaction data to predict and generate a savings plan. By categorizing expenses, calculating total expenditures, and estimating savings based on income and spending habits, we intend to provide insights that can support financial planning and decision-making."15 ]16 },17 {18 "cell_type": "markdown",19 "metadata": {20 "id": "e_Q3Qpl4seW3"21 },22 "source": [23 "# Load Data"24 ]25 },26 {27 "cell_type": "code",28 "execution_count": 19,29 "metadata": {30 "colab": {31 "base_uri": "https://localhost:8080/",32 "height": 20633 },34 "id": "A1luY-T8xeLN",35 "outputId": "a97f331f-d23b-4409-bc6e-cb1ea23ec727"36 },37 "outputs": [38 {39 "data": {40 "application/vnd.google.colaboratory.intrinsic+json": {41 "summary": "{\n \"name\": \"df\",\n \"rows\": 806,\n \"fields\": [\n {\n \"column\": \"Date\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 432,\n \"samples\": [\n \"09/18/2019\",\n \"04/18/2018\",\n \"09/17/2018\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Description\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 65,\n \"samples\": [\n \"German Restaurant\",\n \"Best Buy\",\n \"Amazon\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Amount\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 667.630373777346,\n \"min\": 1.75,\n \"max\": 9200.0,\n \"num_unique_values\": 454,\n \"samples\": [\n 4.21,\n 14.0,\n 957.6\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Transaction Type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"credit\",\n \"debit\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Category\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 22,\n \"samples\": [\n \"Shopping\",\n \"Coffee Shops\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Account Name\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"Platinum Card\",\n \"Checking\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}",42 "type": "dataframe",43 "variable_name": "df"44 },45 "text/html": [46 "\n",47 " <div id=\"df-8dea951d-ecc6-412b-b671-ccaea14f6364\" class=\"colab-df-container\">\n",48 " <div>\n",49 "<style scoped>\n",50 " .dataframe tbody tr th:only-of-type {\n",51 " vertical-align: middle;\n",52 " }\n",53 "\n",54 " .dataframe tbody tr th {\n",55 " vertical-align: top;\n",56 " }\n",57 "\n",58 " .dataframe thead th {\n",59 " text-align: right;\n",60 " }\n",61 "</style>\n",62 "<table border=\"1\" class=\"dataframe\">\n",63 " <thead>\n",64 " <tr style=\"text-align: right;\">\n",65 " <th></th>\n",66 " <th>Date</th>\n",67 " <th>Description</th>\n",68 " <th>Amount</th>\n",69 " <th>Transaction Type</th>\n",70 " <th>Category</th>\n",71 " <th>Account Name</th>\n",72 " </tr>\n",73 " </thead>\n",74 " <tbody>\n",75 " <tr>\n",76 " <th>0</th>\n",77 " <td>01/01/2018</td>\n",78 " <td>Amazon</td>\n",79 " <td>11.11</td>\n",80 " <td>debit</td>\n",81 " <td>Shopping</td>\n",82 " <td>Platinum Card</td>\n",83 " </tr>\n",84 " <tr>\n",85 " <th>1</th>\n",86 " <td>01/02/2018</td>\n",87 " <td>Mortgage Payment</td>\n",88 " <td>1247.44</td>\n",89 " <td>debit</td>\n",90 " <td>Mortgage & Rent</td>\n",91 " <td>Checking</td>\n",92 " </tr>\n",93 " <tr>\n",94 " <th>2</th>\n",95 " <td>01/02/2018</td>\n",96 " <td>Thai Restaurant</td>\n",97 " <td>24.22</td>\n",98 " <td>debit</td>\n",99 " <td>Restaurants</td>\n",100 " <td>Silver Card</td>\n",101 " </tr>\n",102 " <tr>\n",103 " <th>3</th>\n",104 " <td>01/03/2018</td>\n",105 " <td>Credit Card Payment</td>\n",106 " <td>2298.09</td>\n",107 " <td>credit</td>\n",108 " <td>Credit Card Payment</td>\n",109 " <td>Platinum Card</td>\n",110 " </tr>\n",111 " <tr>\n",112 " <th>4</th>\n",113 " <td>01/04/2018</td>\n",114 " <td>Netflix</td>\n",115 " <td>11.76</td>\n",116 " <td>debit</td>\n",117 " <td>Movies & DVDs</td>\n",118 " <td>Platinum Card</td>\n",119 " </tr>\n",120 " </tbody>\n",121 "</table>\n",122 "</div>\n",123 " <div class=\"colab-df-buttons\">\n",124 "\n",125 " <div class=\"colab-df-container\">\n",126 " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8dea951d-ecc6-412b-b671-ccaea14f6364')\"\n",127 " title=\"Convert this dataframe to an interactive table.\"\n",128 " style=\"display:none;\">\n",129 "\n",130 " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",131 " <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",132 " </svg>\n",133 " </button>\n",134 "\n",135 " <style>\n",136 " .colab-df-container {\n",137 " display:flex;\n",138 " gap: 12px;\n",139 " }\n",140 "\n",141 " .colab-df-convert {\n",142 " background-color: #E8F0FE;\n",143 " 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'block' : 'none';\n",181 "\n",182 " async function convertToInteractive(key) {\n",183 " const element = document.querySelector('#df-8dea951d-ecc6-412b-b671-ccaea14f6364');\n",184 " const dataTable =\n",185 " await google.colab.kernel.invokeFunction('convertToInteractive',\n",186 " [key], {});\n",187 " if (!dataTable) return;\n",188 "\n",189 " const docLinkHtml = 'Like what you see? Visit the ' +\n",190 " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",191 " + ' to learn more about interactive tables.';\n",192 " element.innerHTML = '';\n",193 " dataTable['output_type'] = 'display_data';\n",194 " await google.colab.output.renderOutput(dataTable, element);\n",195 " const docLink = document.createElement('div');\n",196 " docLink.innerHTML = docLinkHtml;\n",197 " element.appendChild(docLink);\n",198 " }\n",199 " </script>\n",200 " </div>\n",201 "\n",202 "\n",203 "<div id=\"df-9fa52134-1258-4ed7-9769-68a2f58c41a0\">\n",204 " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9fa52134-1258-4ed7-9769-68a2f58c41a0')\"\n",205 " title=\"Suggest charts\"\n",206 " style=\"display:none;\">\n",207 "\n",208 "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",209 " width=\"24px\">\n",210 " <g>\n",211 " <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",212 " </g>\n",213 "</svg>\n",214 " </button>\n",215 "\n",216 "<style>\n",217 " .colab-df-quickchart {\n",218 " --bg-color: #E8F0FE;\n",219 " --fill-color: #1967D2;\n",220 " --hover-bg-color: #E2EBFA;\n",221 " --hover-fill-color: #174EA6;\n",222 " --disabled-fill-color: #AAA;\n",223 " --disabled-bg-color: #DDD;\n",224 " }\n",225 "\n",226 " [theme=dark] .colab-df-quickchart {\n",227 " --bg-color: #3B4455;\n",228 " --fill-color: #D2E3FC;\n",229 " --hover-bg-color: #434B5C;\n",230 " --hover-fill-color: #FFFFFF;\n",231 " --disabled-bg-color: #3B4455;\n",232 " --disabled-fill-color: #666;\n",233 " }\n",234 "\n",235 " .colab-df-quickchart {\n",236 " background-color: var(--bg-color);\n",237 " border: none;\n",238 " border-radius: 50%;\n",239 " cursor: pointer;\n",240 " display: none;\n",241 " fill: var(--fill-color);\n",242 " height: 32px;\n",243 " padding: 0;\n",244 " width: 32px;\n",245 " }\n",246 "\n",247 " .colab-df-quickchart:hover {\n",248 " background-color: var(--hover-bg-color);\n",249 " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",250 " fill: var(--button-hover-fill-color);\n",251 " }\n",252 "\n",253 " .colab-df-quickchart-complete:disabled,\n",254 " .colab-df-quickchart-complete:disabled:hover {\n",255 " background-color: var(--disabled-bg-color);\n",256 " fill: var(--disabled-fill-color);\n",257 " box-shadow: none;\n",258 " }\n",259 "\n",260 " .colab-df-spinner {\n",261 " border: 2px solid var(--fill-color);\n",262 " border-color: transparent;\n",263 " border-bottom-color: var(--fill-color);\n",264 " animation:\n",265 " spin 1s steps(1) infinite;\n",266 " }\n",267 "\n",268 " @keyframes spin {\n",269 " 0% {\n",270 " border-color: transparent;\n",271 " border-bottom-color: var(--fill-color);\n",272 " border-left-color: var(--fill-color);\n",273 " }\n",274 " 20% {\n",275 " border-color: transparent;\n",276 " border-left-color: var(--fill-color);\n",277 " border-top-color: var(--fill-color);\n",278 " }\n",279 " 30% {\n",280 " border-color: transparent;\n",281 " border-left-color: var(--fill-color);\n",282 " border-top-color: var(--fill-color);\n",283 " border-right-color: var(--fill-color);\n",284 " }\n",285 " 40% {\n",286 " border-color: transparent;\n",287 " border-right-color: var(--fill-color);\n",288 " border-top-color: var(--fill-color);\n",289 " }\n",290 " 60% {\n",291 " border-color: transparent;\n",292 " border-right-color: var(--fill-color);\n",293 " }\n",294 " 80% {\n",295 " border-color: transparent;\n",296 " border-right-color: var(--fill-color);\n",297 " border-bottom-color: var(--fill-color);\n",298 " }\n",299 " 90% {\n",300 " border-color: transparent;\n",301 " border-bottom-color: var(--fill-color);\n",302 " }\n",303 " }\n",304 "</style>\n",305 "\n",306 " <script>\n",307 " async function quickchart(key) {\n",308 " const quickchartButtonEl =\n",309 " document.querySelector('#' + key + ' button');\n",310 " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",311 " quickchartButtonEl.classList.add('colab-df-spinner');\n",312 " try {\n",313 " const charts = await google.colab.kernel.invokeFunction(\n",314 " 'suggestCharts', [key], {});\n",315 " } catch (error) {\n",316 " console.error('Error during call to suggestCharts:', error);\n",317 " }\n",318 " quickchartButtonEl.classList.remove('colab-df-spinner');\n",319 " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",320 " }\n",321 " (() => {\n",322 " let quickchartButtonEl =\n",323 " document.querySelector('#df-9fa52134-1258-4ed7-9769-68a2f58c41a0 button');\n",324 " quickchartButtonEl.style.display =\n",325 " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",326 " })();\n",327 " </script>\n",328 "</div>\n",329 "\n",330 " </div>\n",331 " </div>\n"332 ],333 "text/plain": [334 " Date Description Amount Transaction Type \\\n",335 "0 01/01/2018 Amazon 11.11 debit \n",336 "1 01/02/2018 Mortgage Payment 1247.44 debit \n",337 "2 01/02/2018 Thai Restaurant 24.22 debit \n",338 "3 01/03/2018 Credit Card Payment 2298.09 credit \n",339 "4 01/04/2018 Netflix 11.76 debit \n",340 "\n",341 " Category Account Name \n",342 "0 Shopping Platinum Card \n",343 "1 Mortgage & Rent Checking \n",344 "2 Restaurants Silver Card \n",345 "3 Credit Card Payment Platinum Card \n",346 "4 Movies & DVDs Platinum Card "347 ]348 },349 "execution_count": 19,350 "metadata": {},351 "output_type": "execute_result"352 }353 ],354 "source": [355 "import pandas as pd\n",356 "\n",357 "df= pd.read_csv('/content/personal_transactions.csv')\n",358 "\n",359 "df.head()"360 ]361 },362 {363 "cell_type": "markdown",364 "metadata": {365 "id": "5sbUeMAQt2I-"366 },367 "source": [368 "# Data Understanding"369 ]370 },371 {372 "cell_type": "code",373 "execution_count": 20,374 "metadata": {375 "colab": {376 "base_uri": "https://localhost:8080/"377 },378 "id": "jx2WhXqgtshc",379 "outputId": "22cb290b-0899-42b1-c022-d0a66ed0cb1b"380 },381 "outputs": [382 {383 "name": "stdout",384 "output_type": "stream",385 "text": [386 "<class 'pandas.core.frame.DataFrame'>\n",387 "RangeIndex: 806 entries, 0 to 805\n",388 "Data columns (total 6 columns):\n",389 " # Column Non-Null Count Dtype \n",390 "--- ------ -------------- ----- \n",391 " 0 Date 806 non-null object \n",392 " 1 Description 806 non-null object \n",393 " 2 Amount 806 non-null float64\n",394 " 3 Transaction Type 806 non-null object \n",395 " 4 Category 806 non-null object \n",396 " 5 Account Name 806 non-null object \n",397 "dtypes: float64(1), object(5)\n",398 "memory usage: 37.9+ KB\n"399 ]400 }401 ],402 "source": [403 "#Get concise information of each column in dataset\n",404 "df.info()"405 ]406 },407 {408 "cell_type": "code",409 "execution_count": 21,410 "metadata": {411 "colab": {412 "base_uri": "https://localhost:8080/"413 },414 "id": "EIV6ycuFuD6H",415 "outputId": "9df4c5b7-edfb-473b-f42e-1d4597921ac0"416 },417 "outputs": [418 {419 "data": {420 "text/plain": [421 "Date 0\n",422 "Description 0\n",423 "Amount 0\n",424 "Transaction Type 0\n",425 "Category 0\n",426 "Account Name 0\n",427 "dtype: int64"428 ]429 },430 "execution_count": 21,431 "metadata": {},432 "output_type": "execute_result"433 }434 ],435 "source": [436 "#Check for missing values in all columns\n",437 "df.isnull().sum()"438 ]439 },440 {441 "cell_type": "markdown",442 "metadata": {443 "id": "DFtNqGXIu9G5"444 },445 "source": [446 "# Data Cleaning"447 ]448 },449 {450 "cell_type": "code",451 "execution_count": 22,452 "metadata": {453 "id": "f2XMfKUMyj7p"454 },455 "outputs": [],456 "source": [457 "# Convert all string columns to lowercase\n",458 "for col in df.select_dtypes(include='object'):\n",459 " df[col] = df[col].str.lower()"460 ]461 },462 {463 "cell_type": "code",464 "execution_count": 23,465 "metadata": {466 "colab": {467 "base_uri": "https://localhost:8080/",468 "height": 206469 },470 "id": "OcMb-4OIvFyG",471 "outputId": "fb3605e5-7232-4741-eacd-83816914b8d9"472 },473 "outputs": [474 {475 "data": {476 "application/vnd.google.colaboratory.intrinsic+json": {477 "summary": "{\n \"name\": \"df\",\n \"rows\": 806,\n \"fields\": [\n {\n \"column\": \"Date\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 432,\n \"samples\": [\n \"09/18/2019\",\n \"04/18/2018\",\n \"09/17/2018\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Amount\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 667.630373777346,\n \"min\": 1.75,\n \"max\": 9200.0,\n \"num_unique_values\": 454,\n \"samples\": [\n 4.21,\n 14.0,\n 957.6\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Transaction Type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"credit\",\n \"debit\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Category\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 22,\n \"samples\": [\n \"shopping\",\n \"coffee shops\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}",478 "type": "dataframe",479 "variable_name": "df"480 },481 "text/html": [482 "\n",483 " <div id=\"df-7e8bc91b-9115-42be-883b-77dc5761d16c\" class=\"colab-df-container\">\n",484 " <div>\n",485 "<style scoped>\n",486 " .dataframe tbody tr th:only-of-type {\n",487 " vertical-align: middle;\n",488 " }\n",489 "\n",490 " .dataframe tbody tr th {\n",491 " vertical-align: top;\n",492 " }\n",493 "\n",494 " .dataframe thead th {\n",495 " text-align: right;\n",496 " }\n",497 "</style>\n",498 "<table border=\"1\" class=\"dataframe\">\n",499 " <thead>\n",500 " <tr style=\"text-align: right;\">\n",501 " <th></th>\n",502 " <th>Date</th>\n",503 " <th>Amount</th>\n",504 " <th>Transaction Type</th>\n",505 " <th>Category</th>\n",506 " </tr>\n",507 " </thead>\n",508 " <tbody>\n",509 " <tr>\n",510 " <th>0</th>\n",511 " <td>01/01/2018</td>\n",512 " <td>11.11</td>\n",513 " <td>debit</td>\n",514 " <td>shopping</td>\n",515 " </tr>\n",516 " <tr>\n",517 " <th>1</th>\n",518 " <td>01/02/2018</td>\n",519 " <td>1247.44</td>\n",520 " <td>debit</td>\n",521 " <td>mortgage & rent</td>\n",522 " </tr>\n",523 " <tr>\n",524 " <th>2</th>\n",525 " <td>01/02/2018</td>\n",526 " <td>24.22</td>\n",527 " <td>debit</td>\n",528 " <td>restaurants</td>\n",529 " </tr>\n",530 " <tr>\n",531 " <th>3</th>\n",532 " <td>01/03/2018</td>\n",533 " <td>2298.09</td>\n",534 " <td>credit</td>\n",535 " <td>credit card payment</td>\n",536 " </tr>\n",537 " <tr>\n",538 " <th>4</th>\n",539 " <td>01/04/2018</td>\n",540 " <td>11.76</td>\n",541 " <td>debit</td>\n",542 " <td>movies & dvds</td>\n",543 " </tr>\n",544 " </tbody>\n",545 "</table>\n",546 "</div>\n",547 " <div class=\"colab-df-buttons\">\n",548 "\n",549 " <div class=\"colab-df-container\">\n",550 " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7e8bc91b-9115-42be-883b-77dc5761d16c')\"\n",551 " title=\"Convert this dataframe to an interactive table.\"\n",552 " style=\"display:none;\">\n",553 "\n",554 " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",555 " <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",556 " </svg>\n",557 " </button>\n",558 "\n",559 " <style>\n",560 " .colab-df-container {\n",561 " display:flex;\n",562 " gap: 12px;\n",563 " }\n",564 "\n",565 " .colab-df-convert {\n",566 " background-color: #E8F0FE;\n",567 " border: none;\n",568 " border-radius: 50%;\n",569 " cursor: pointer;\n",570 " 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google.colab.kernel.accessAllowed ? 'block' : 'none';\n",605 "\n",606 " async function convertToInteractive(key) {\n",607 " const element = document.querySelector('#df-7e8bc91b-9115-42be-883b-77dc5761d16c');\n",608 " const dataTable =\n",609 " await google.colab.kernel.invokeFunction('convertToInteractive',\n",610 " [key], {});\n",611 " if (!dataTable) return;\n",612 "\n",613 " const docLinkHtml = 'Like what you see? Visit the ' +\n",614 " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",615 " + ' to learn more about interactive tables.';\n",616 " element.innerHTML = '';\n",617 " dataTable['output_type'] = 'display_data';\n",618 " await google.colab.output.renderOutput(dataTable, element);\n",619 " const docLink = document.createElement('div');\n",620 " docLink.innerHTML = docLinkHtml;\n",621 " element.appendChild(docLink);\n",622 " }\n",623 " </script>\n",624 " </div>\n",625 "\n",626 "\n",627 "<div id=\"df-46891b1d-22ca-4335-96cf-8aa6c6b97342\">\n",628 " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-46891b1d-22ca-4335-96cf-8aa6c6b97342')\"\n",629 " title=\"Suggest charts\"\n",630 " style=\"display:none;\">\n",631 "\n",632 "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",633 " width=\"24px\">\n",634 " <g>\n",635 " <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",636 " </g>\n",637 "</svg>\n",638 " </button>\n",639 "\n",640 "<style>\n",641 " .colab-df-quickchart {\n",642 " --bg-color: #E8F0FE;\n",643 " --fill-color: #1967D2;\n",644 " --hover-bg-color: #E2EBFA;\n",645 " --hover-fill-color: #174EA6;\n",646 " --disabled-fill-color: #AAA;\n",647 " --disabled-bg-color: #DDD;\n",648 " }\n",649 "\n",650 " [theme=dark] .colab-df-quickchart {\n",651 " --bg-color: #3B4455;\n",652 " --fill-color: #D2E3FC;\n",653 " --hover-bg-color: #434B5C;\n",654 " --hover-fill-color: #FFFFFF;\n",655 " --disabled-bg-color: #3B4455;\n",656 " --disabled-fill-color: #666;\n",657 " }\n",658 "\n",659 " .colab-df-quickchart {\n",660 " background-color: var(--bg-color);\n",661 " border: none;\n",662 " border-radius: 50%;\n",663 " cursor: pointer;\n",664 " display: none;\n",665 " fill: var(--fill-color);\n",666 " height: 32px;\n",667 " padding: 0;\n",668 " width: 32px;\n",669 " }\n",670 "\n",671 " .colab-df-quickchart:hover {\n",672 " background-color: var(--hover-bg-color);\n",673 " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",674 " fill: var(--button-hover-fill-color);\n",675 " }\n",676 "\n",677 " .colab-df-quickchart-complete:disabled,\n",678 " .colab-df-quickchart-complete:disabled:hover {\n",679 " background-color: var(--disabled-bg-color);\n",680 " fill: var(--disabled-fill-color);\n",681 " box-shadow: none;\n",682 " }\n",683 "\n",684 " .colab-df-spinner {\n",685 " border: 2px solid var(--fill-color);\n",686 " border-color: transparent;\n",687 " border-bottom-color: var(--fill-color);\n",688 " animation:\n",689 " spin 1s steps(1) infinite;\n",690 " }\n",691 "\n",692 " @keyframes spin {\n",693 " 0% {\n",694 " border-color: transparent;\n",695 " border-bottom-color: var(--fill-color);\n",696 " border-left-color: var(--fill-color);\n",697 " }\n",698 " 20% {\n",699 " border-color: transparent;\n",700 " border-left-color: var(--fill-color);\n",701 " border-top-color: var(--fill-color);\n",702 " }\n",703 " 30% {\n",704 " border-color: transparent;\n",705 " border-left-color: var(--fill-color);\n",706 " border-top-color: var(--fill-color);\n",707 " border-right-color: var(--fill-color);\n",708 " }\n",709 " 40% {\n",710 " border-color: transparent;\n",711 " border-right-color: var(--fill-color);\n",712 " border-top-color: var(--fill-color);\n",713 " }\n",714 " 60% {\n",715 " border-color: transparent;\n",716 " border-right-color: var(--fill-color);\n",717 " }\n",718 " 80% {\n",719 " border-color: transparent;\n",720 " border-right-color: var(--fill-color);\n",721 " border-bottom-color: var(--fill-color);\n",722 " }\n",723 " 90% {\n",724 " border-color: transparent;\n",725 " border-bottom-color: var(--fill-color);\n",726 " }\n",727 " }\n",728 "</style>\n",729 "\n",730 " <script>\n",731 " async function quickchart(key) {\n",732 " const quickchartButtonEl =\n",733 " document.querySelector('#' + key + ' button');\n",734 " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",735 " quickchartButtonEl.classList.add('colab-df-spinner');\n",736 " try {\n",737 " const charts = await google.colab.kernel.invokeFunction(\n",738 " 'suggestCharts', [key], {});\n",739 " } catch (error) {\n",740 " console.error('Error during call to suggestCharts:', error);\n",741 " }\n",742 " quickchartButtonEl.classList.remove('colab-df-spinner');\n",743 " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",744 " }\n",745 " (() => {\n",746 " let quickchartButtonEl =\n",747 " document.querySelector('#df-46891b1d-22ca-4335-96cf-8aa6c6b97342 button');\n",748 " quickchartButtonEl.style.display =\n",749 " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",750 " })();\n",751 " </script>\n",752 "</div>\n",753 "\n",754 " </div>\n",755 " </div>\n"756 ],757 "text/plain": [758 " Date Amount Transaction Type Category\n",759 "0 01/01/2018 11.11 debit shopping\n",760 "1 01/02/2018 1247.44 debit mortgage & rent\n",761 "2 01/02/2018 24.22 debit restaurants\n",762 "3 01/03/2018 2298.09 credit credit card payment\n",763 "4 01/04/2018 11.76 debit movies & dvds"764 ]765 },766 "execution_count": 23,767 "metadata": {},768 "output_type": "execute_result"769 }770 ],771 "source": [772 "#Drop unnecessary column\n",773 "df = df.drop(['Account Name', 'Description'], axis=1)\n",774 "df.head()"775 ]776 },777 {778 "cell_type": "markdown",779 "metadata": {780 "id": "-8yGJiZZvwIG"781 },782 "source": [783 "# Feature Engineering"784 ]785 },786 {787 "cell_type": "code",788 "execution_count": 24,789 "metadata": {790 "colab": {791 "base_uri": "https://localhost:8080/",792 "height": 424793 },794 "id": "j4klz4Of0PN5",795 "outputId": "cee00292-67ce-4bf9-efd6-0396636adedb"796 },797 "outputs": [798 {799 "data": {800 "application/vnd.google.colaboratory.intrinsic+json": {801 "repr_error": "0",802 "type": "dataframe",803 "variable_name": "income"804 },805 "text/html": [806 "\n",807 " <div id=\"df-a578bcbc-397e-4535-b553-5d190a6dec17\" class=\"colab-df-container\">\n",808 " <div>\n",809 "<style scoped>\n",810 " .dataframe tbody tr th:only-of-type {\n",811 " vertical-align: middle;\n",812 " }\n",813 "\n",814 " .dataframe tbody tr th {\n",815 " vertical-align: top;\n",816 " }\n",817 "\n",818 " .dataframe thead th {\n",819 " text-align: right;\n",820 " }\n",821 "</style>\n",822 "<table border=\"1\" class=\"dataframe\">\n",823 " <thead>\n",824 " <tr style=\"text-align: right;\">\n",825 " <th></th>\n",826 " <th>Date</th>\n",827 " <th>Amount</th>\n",828 " <th>Transaction Type</th>\n",829 " <th>Category</th>\n",830 " </tr>\n",831 " </thead>\n",832 " <tbody>\n",833 " <tr>\n",834 " <th>3</th>\n",835 " <td>01/03/2018</td>\n",836 " <td>2298.09</td>\n",837 " <td>credit</td>\n",838 " <td>credit card payment</td>\n",839 " </tr>\n",840 " <tr>\n",841 " <th>13</th>\n",842 " <td>01/12/2018</td>\n",843 " <td>2000.00</td>\n",844 " <td>credit</td>\n",845 " <td>paycheck</td>\n",846 " </tr>\n",847 " <tr>\n",848 " <th>20</th>\n",849 " <td>01/19/2018</td>\n",850 " <td>2000.00</td>\n",851 " <td>credit</td>\n",852 " <td>paycheck</td>\n",853 " </tr>\n",854 " <tr>\n",855 " <th>22</th>\n",856 " <td>01/22/2018</td>\n",857 " <td>554.99</td>\n",858 " <td>credit</td>\n",859 " <td>credit card payment</td>\n",860 " </tr>\n",861 " <tr>\n",862 " <th>23</th>\n",863 " <td>01/22/2018</td>\n",864 " <td>309.81</td>\n",865 " <td>credit</td>\n",866 " <td>credit card payment</td>\n",867 " </tr>\n",868 " <tr>\n",869 " <th>...</th>\n",870 " <td>...</td>\n",871 " <td>...</td>\n",872 " <td>...</td>\n",873 " <td>...</td>\n",874 " </tr>\n",875 " <tr>\n",876 " <th>784</th>\n",877 " <td>09/13/2019</td>\n",878 " <td>2250.00</td>\n",879 " <td>credit</td>\n",880 " <td>paycheck</td>\n",881 " </tr>\n",882 " <tr>\n",883 " <th>788</th>\n",884 " <td>09/16/2019</td>\n",885 " <td>90.57</td>\n",886 " <td>credit</td>\n",887 " <td>credit card payment</td>\n",888 " </tr>\n",889 " <tr>\n",890 " <th>790</th>\n",891 " <td>09/17/2019</td>\n",892 " <td>186.13</td>\n",893 " <td>credit</td>\n",894 " <td>credit card payment</td>\n",895 " </tr>\n",896 " <tr>\n",897 " <th>796</th>\n",898 " <td>09/20/2019</td>\n",899 " <td>9.43</td>\n",900 " <td>credit</td>\n",901 " <td>credit card payment</td>\n",902 " </tr>\n",903 " <tr>\n",904 " <th>801</th>\n",905 " <td>09/27/2019</td>\n",906 " <td>2250.00</td>\n",907 " <td>credit</td>\n",908 " <td>paycheck</td>\n",909 " </tr>\n",910 " </tbody>\n",911 "</table>\n",912 "<p>118 rows × 4 columns</p>\n",913 "</div>\n",914 " <div class=\"colab-df-buttons\">\n",915 "\n",916 " <div class=\"colab-df-container\">\n",917 " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a578bcbc-397e-4535-b553-5d190a6dec17')\"\n",918 " title=\"Convert this dataframe to an interactive table.\"\n",919 " style=\"display:none;\">\n",920 "\n",921 " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",922 " <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",923 " </svg>\n",924 " </button>\n",925 "\n",926 " <style>\n",927 " .colab-df-container {\n",928 " display:flex;\n",929 " gap: 12px;\n",930 " }\n",931 "\n",932 " .colab-df-convert {\n",933 " background-color: #E8F0FE;\n",934 " border: none;\n",935 " border-radius: 50%;\n",936 " cursor: pointer;\n",937 " display: none;\n",938 " fill: #1967D2;\n",939 " height: 32px;\n",940 " padding: 0 0 0 0;\n",941 " width: 32px;\n",942 " }\n",943 "\n",944 " .colab-df-convert:hover {\n",945 " background-color: #E2EBFA;\n",946 " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",947 " fill: #174EA6;\n",948 " }\n",949 "\n",950 " .colab-df-buttons div {\n",951 " margin-bottom: 4px;\n",952 " }\n",953 "\n",954 " [theme=dark] .colab-df-convert {\n",955 " background-color: #3B4455;\n",956 " fill: #D2E3FC;\n",957 " }\n",958 "\n",959 " [theme=dark] .colab-df-convert:hover {\n",960 " background-color: #434B5C;\n",961 " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",962 " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",963 " fill: #FFFFFF;\n",964 " }\n",965 " </style>\n",966 "\n",967 " <script>\n",968 " const buttonEl =\n",969 " document.querySelector('#df-a578bcbc-397e-4535-b553-5d190a6dec17 button.colab-df-convert');\n",970 " buttonEl.style.display =\n",971 " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",972 "\n",973 " async function convertToInteractive(key) {\n",974 " const element = document.querySelector('#df-a578bcbc-397e-4535-b553-5d190a6dec17');\n",975 " const dataTable =\n",976 " await google.colab.kernel.invokeFunction('convertToInteractive',\n",977 " [key], {});\n",978 " if (!dataTable) return;\n",979 "\n",980 " const docLinkHtml = 'Like what you see? Visit the ' +\n",981 " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",982 " + ' to learn more about interactive tables.';\n",983 " element.innerHTML = '';\n",984 " dataTable['output_type'] = 'display_data';\n",985 " await google.colab.output.renderOutput(dataTable, element);\n",986 " const docLink = document.createElement('div');\n",987 " docLink.innerHTML = docLinkHtml;\n",988 " element.appendChild(docLink);\n",989 " }\n",990 " </script>\n",991 " </div>\n",992 "\n",993 "\n",994 "<div id=\"df-aabd6aba-88fe-44ca-af31-744390e27fdc\">\n",995 " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-aabd6aba-88fe-44ca-af31-744390e27fdc')\"\n",996 " title=\"Suggest charts\"\n",997 " style=\"display:none;\">\n",998 "\n",999 "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",1000 " width=\"24px\">\n",1001 " <g>\n",1002 " <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",1003 " </g>\n",1004 "</svg>\n",1005 " </button>\n",1006 "\n",1007 "<style>\n",1008 " .colab-df-quickchart {\n",1009 " --bg-color: #E8F0FE;\n",1010 " --fill-color: #1967D2;\n",1011 " --hover-bg-color: #E2EBFA;\n",1012 " --hover-fill-color: #174EA6;\n",1013 " --disabled-fill-color: #AAA;\n",1014 " --disabled-bg-color: #DDD;\n",1015 " }\n",1016 "\n",1017 " [theme=dark] .colab-df-quickchart {\n",1018 " --bg-color: #3B4455;\n",1019 " --fill-color: #D2E3FC;\n",1020 " --hover-bg-color: #434B5C;\n",1021 " --hover-fill-color: #FFFFFF;\n",1022 " --disabled-bg-color: #3B4455;\n",1023 " --disabled-fill-color: #666;\n",1024 " }\n",1025 "\n",1026 " .colab-df-quickchart {\n",1027 " background-color: var(--bg-color);\n",1028 " border: none;\n",1029 " border-radius: 50%;\n",1030 " cursor: pointer;\n",1031 " display: none;\n",1032 " fill: var(--fill-color);\n",1033 " height: 32px;\n",1034 " padding: 0;\n",1035 " width: 32px;\n",1036 " }\n",1037 "\n",1038 " .colab-df-quickchart:hover {\n",1039 " background-color: var(--hover-bg-color);\n",1040 " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",1041 " fill: var(--button-hover-fill-color);\n",1042 " }\n",1043 "\n",1044 " .colab-df-quickchart-complete:disabled,\n",1045 " .colab-df-quickchart-complete:disabled:hover {\n",1046 " background-color: var(--disabled-bg-color);\n",1047 " fill: var(--disabled-fill-color);\n",1048 " box-shadow: none;\n",1049 " }\n",1050 "\n",1051 " .colab-df-spinner {\n",1052 " border: 2px solid var(--fill-color);\n",1053 " border-color: transparent;\n",1054 " border-bottom-color: var(--fill-color);\n",1055 " animation:\n",1056 " spin 1s steps(1) infinite;\n",1057 " }\n",1058 "\n",1059 " @keyframes spin {\n",1060 " 0% {\n",1061 " border-color: transparent;\n",1062 " border-bottom-color: var(--fill-color);\n",1063 " border-left-color: var(--fill-color);\n",1064 " }\n",1065 " 20% {\n",1066 " border-color: transparent;\n",1067 " border-left-color: var(--fill-color);\n",1068 " border-top-color: var(--fill-color);\n",1069 " }\n",1070 " 30% {\n",1071 " border-color: transparent;\n",1072 " border-left-color: var(--fill-color);\n",1073 " border-top-color: var(--fill-color);\n",1074 " border-right-color: var(--fill-color);\n",1075 " }\n",1076 " 40% {\n",1077 " border-color: transparent;\n",1078 " border-right-color: var(--fill-color);\n",1079 " border-top-color: var(--fill-color);\n",1080 " }\n",1081 " 60% {\n",1082 " border-color: transparent;\n",1083 " border-right-color: var(--fill-color);\n",1084 " }\n",1085 " 80% {\n",1086 " border-color: transparent;\n",1087 " border-right-color: var(--fill-color);\n",1088 " border-bottom-color: var(--fill-color);\n",1089 " }\n",1090 " 90% {\n",1091 " border-color: transparent;\n",1092 " border-bottom-color: var(--fill-color);\n",1093 " }\n",1094 " }\n",1095 "</style>\n",1096 "\n",1097 " <script>\n",1098 " async function quickchart(key) {\n",1099 " const quickchartButtonEl =\n",1100 " document.querySelector('#' + key + ' button');\n",1101 " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",1102 " quickchartButtonEl.classList.add('colab-df-spinner');\n",1103 " try {\n",1104 " const charts = await google.colab.kernel.invokeFunction(\n",1105 " 'suggestCharts', [key], {});\n",1106 " } catch (error) {\n",1107 " console.error('Error during call to suggestCharts:', error);\n",1108 " }\n",1109 " quickchartButtonEl.classList.remove('colab-df-spinner');\n",1110 " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",1111 " }\n",1112 " (() => {\n",1113 " let quickchartButtonEl =\n",1114 " document.querySelector('#df-aabd6aba-88fe-44ca-af31-744390e27fdc button');\n",1115 " quickchartButtonEl.style.display =\n",1116 " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",1117 " })();\n",1118 " </script>\n",1119 "</div>\n",1120 "\n",1121 " <div id=\"id_fcaf0cb1-8de6-4025-b301-ff5b2f700e45\">\n",1122 " <style>\n",1123 " .colab-df-generate {\n",1124 " background-color: #E8F0FE;\n",1125 " border: none;\n",1126 " border-radius: 50%;\n",1127 " cursor: pointer;\n",1128 " display: none;\n",1129 " fill: #1967D2;\n",1130 " height: 32px;\n",1131 " padding: 0 0 0 0;\n",1132 " width: 32px;\n",1133 " }\n",1134 "\n",1135 " .colab-df-generate:hover {\n",1136 " background-color: #E2EBFA;\n",1137 " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",1138 " fill: #174EA6;\n",1139 " }\n",1140 "\n",1141 " [theme=dark] .colab-df-generate {\n",1142 " background-color: #3B4455;\n",1143 " fill: #D2E3FC;\n",1144 " }\n",1145 "\n",1146 " [theme=dark] .colab-df-generate:hover {\n",1147 " background-color: #434B5C;\n",1148 " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",1149 " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",1150 " fill: #FFFFFF;\n",1151 " }\n",1152 " </style>\n",1153 " <button class=\"colab-df-generate\" onclick=\"generateWithVariable('income')\"\n",1154 " title=\"Generate code using this dataframe.\"\n",1155 " style=\"display:none;\">\n",1156 "\n",1157 " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",1158 " width=\"24px\">\n",1159 " <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",1160 " </svg>\n",1161 " </button>\n",1162 " <script>\n",1163 " (() => {\n",1164 " const buttonEl =\n",1165 " document.querySelector('#id_fcaf0cb1-8de6-4025-b301-ff5b2f700e45 button.colab-df-generate');\n",1166 " buttonEl.style.display =\n",1167 " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",1168 "\n",1169 " buttonEl.onclick = () => {\n",1170 " google.colab.notebook.generateWithVariable('income');\n",1171 " }\n",1172 " })();\n",1173 " </script>\n",1174 " </div>\n",1175 "\n",1176 " </div>\n",1177 " </div>\n"1178 ],1179 "text/plain": [1180 " Date Amount Transaction Type Category\n",1181 "3 01/03/2018 2298.09 credit credit card payment\n",1182 "13 01/12/2018 2000.00 credit paycheck\n",1183 "20 01/19/2018 2000.00 credit paycheck\n",1184 "22 01/22/2018 554.99 credit credit card payment\n",1185 "23 01/22/2018 309.81 credit credit card payment\n",1186 ".. ... ... ... ...\n",1187 "784 09/13/2019 2250.00 credit paycheck\n",1188 "788 09/16/2019 90.57 credit credit card payment\n",1189 "790 09/17/2019 186.13 credit credit card payment\n",1190 "796 09/20/2019 9.43 credit credit card payment\n",1191 "801 09/27/2019 2250.00 credit paycheck\n",1192 "\n",1193 "[118 rows x 4 columns]"1194 ]1195 },1196 "execution_count": 24,1197 "metadata": {},1198 "output_type": "execute_result"1199 }1200 ],