This project uses Machine Learning Regression<\/strong> to predict your future monthly expenses<\/strong> based on your past spending patterns.<\/p>\n\n\n\n It\u2019s similar to what budgeting apps (like MoneyView, Walnut, YNAB) use.<\/p>\n\n\n\n \u2714 Learn from your last 6\u201312 months of expenses Using Linear Regression<\/strong> (best for simple numeric trends):<\/p>\n\n\n\n Using RandomForestRegressor<\/strong> for higher accuracy:<\/p>\n\n\n\n This project uses Machine Learning Regression to predict your future monthly expenses based on your past spending patterns. It\u2019s similar to what budgeting apps (like MoneyView, Walnut, YNAB) use. \ud83c\udfaf What This Project Can Do \u2714 Learn from your last 6\u201312 months of expenses\u2714 Predict next month\u2019s spending\u2714 Show category-wise predictions (food, rent, travel)\u2714 Visualize […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-272","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/codetypingpro.com\/index.php?rest_route=\/wp\/v2\/posts\/272","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/codetypingpro.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/codetypingpro.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/codetypingpro.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/codetypingpro.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=272"}],"version-history":[{"count":1,"href":"https:\/\/codetypingpro.com\/index.php?rest_route=\/wp\/v2\/posts\/272\/revisions"}],"predecessor-version":[{"id":273,"href":"https:\/\/codetypingpro.com\/index.php?rest_route=\/wp\/v2\/posts\/272\/revisions\/273"}],"wp:attachment":[{"href":"https:\/\/codetypingpro.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=272"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/codetypingpro.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=272"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/codetypingpro.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=272"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\n\n\n\n\ud83c\udfaf What This Project Can Do<\/strong><\/h1>\n\n\n\n
\u2714 Predict next month\u2019s spending
\u2714 Show category-wise predictions (food, rent, travel)
\u2714 Visualize data
\u2714 Support CSV or SQL database
\u2714 Optionally build a mobile-friendly Streamlit dashboard<\/p>\n\n\n\n
\n\n\n\n\ud83d\udcc1 Folder Structure<\/h1>\n\n\n\n
expense_predictor\/\n\u2502\u2500\u2500 expense_data.csv\n\u2502\u2500\u2500 train_model.py\n\u2502\u2500\u2500 predict.py\n\u2502\u2500\u2500 model.pkl\n\u2502\u2500\u2500 vectorizer.pkl (if using categories)\n<\/code><\/pre>\n\n\n\n
\n\n\n\n\ud83d\udcca Example CSV (expense_data.csv)<\/h1>\n\n\n\n
month,expense\n1,12000\n2,13000\n3,15000\n4,14500\n5,16000\n6,17000\n<\/code><\/pre>\n\n\n\n
\n\n\n\n\ud83e\udde0 1. ML Model Training (train_model.py)<\/strong><\/h1>\n\n\n\n
import pandas as pd\nfrom sklearn.linear_model import LinearRegression\nimport joblib\n\ndf = pd.read_csv(\"expense_data.csv\")\n\nX = df[[\"month\"]]\ny = df[\"expense\"]\n\nmodel = LinearRegression()\nmodel.fit(X, y)\n\njoblib.dump(model, \"model.pkl\")\nprint(\"Model trained and saved as model.pkl\")\n<\/code><\/pre>\n\n\n\n
\n\n\n\n\ud83e\udde0 2. Predict Next Month (predict.py)<\/strong><\/h1>\n\n\n\n
import joblib\nimport pandas as pd\n\ndf = pd.read_csv(\"expense_data.csv\")\nnext_month = df[\"month\"].max() + 1\n\nmodel = joblib.load(\"model.pkl\")\nprediction = model.predict([[next_month]])\n\nprint(f\"Predicted Expense for Month {next_month}: \u20b9{prediction[0]:.2f}\")\n<\/code><\/pre>\n\n\n\n
\n\n\n\n\ud83d\udcc8 Optional Enhanced Dataset (category-based)<\/h1>\n\n\n\n
date,food,travel,rent,shopping,others\n2025-01-01,2200,300,8500,0,200\n2025-02-01,2500,450,8500,500,300\n<\/code><\/pre>\n\n\n\n
\n\n\n\n\ud83e\udde0 Advanced ML Version (Multiple Features)<\/strong><\/h1>\n\n\n\n
from sklearn.ensemble import RandomForestRegressor\nimport pandas as pd\nimport joblib\n\ndf = pd.read_csv(\"expense_data.csv\")\n\nX = df.drop(columns=[\"total\"])\ny = df[\"total\"]\n\nmodel = RandomForestRegressor()\nmodel.fit(X, y)\n\njoblib.dump(model, \"model.pkl\")\nprint(\"Advanced model trained!\")\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"