{"slug": "better-data-beats-better-algorithms-before-changing-the-model-change-the-data", "title": "Better Data Beats Better Algorithms: Before Changing the Model, Change the Data", "summary": "A developer found that improving data quality through feature engineering boosted a Logistic Regression model's accuracy from 72% to 86%—a 14-percentage-point gain—without changing the algorithm. By handling missing values with KNN imputation, removing outliers via IQR, encoding categorical variables, and scaling numerical features, the same model performed dramatically better. The project demonstrates that better data often beats better algorithms in machine learning.", "body_md": "*How Feature Engineering Taught Me That Better Data Often Beats Better Algorithms*\n\nWhen I first started learning Machine Learning, I believed what many beginners believe:\n\nIf my model is not performing well, I need a better algorithm.\n\nSo I kept switching models.\n\nI moved from Logistic Regression to Decision Trees, then Random Forest, and later even started reading about XGBoost and Neural Networks.\n\nThe results improved slightly, but never dramatically.\n\nWhat surprised me was that the biggest improvement didn't come from changing the algorithm.\n\nIt came from changing the data.\n\nI was working on a dataset containing missing values, outliers, and categorical variables.\n\nLike many beginners, my first instinct was simple:\n\n```\nmodel.fit(X_train, y_train)\npred = model.predict(X_test)\n```\n\nThe model trained successfully.\n\nThe accuracy looked acceptable.\n\nBut something felt wrong.\n\nThe data itself was messy.\n\nSome columns contained missing values.\n\nSome numerical features had extreme outliers.\n\nSeveral categorical columns were represented as text.\n\nYet I expected the model to magically learn everything.\n\nI trained a Logistic Regression model on the raw dataset.\n\nResults:\n\n```\nAccuracy : 72%\n```\n\nNot terrible.\n\nNot impressive either.\n\nInstead of changing the model, I decided to investigate the data.\n\nThis turned out to be the most important decision of the entire project.\n\nThe dataset contained several missing values.\n\nAt first I considered simply deleting rows.\n\n```\ndf.dropna(inplace=True)\n```\n\nThe problem?\n\nI lost a significant portion of the data.\n\nSo I experimented with multiple approaches:\n\n``` python\nfrom sklearn.impute import SimpleImputer\n\nimputer = SimpleImputer(strategy='mean')\nX = imputer.fit_transform(X)\nimputer = SimpleImputer(strategy='median')\npython\nfrom sklearn.impute import KNNImputer\n\nimputer = KNNImputer(n_neighbors=5)\nX = imputer.fit_transform(X)\n```\n\nKNN preserved relationships between records much better than simple averaging.\n\nThis alone improved performance.\n\nI then visualized the numerical columns.\n\nThe boxplots looked terrible.\n\nA few extreme values were stretching entire distributions.\n\n```\nsns.boxplot(df[\"experience\"])\n```\n\nThe model was spending too much effort trying to fit a handful of unusual observations.\n\nI used IQR-based treatment.\n\n```\nQ1 = df[\"experience\"].quantile(0.25)\nQ3 = df[\"experience\"].quantile(0.75)\n\nIQR = Q3 - Q1\n\nlower = Q1 - 1.5 * IQR\nupper = Q3 + 1.5 * IQR\n\ndf = df[(df[\"experience\"] >= lower) &\n        (df[\"experience\"] <= upper)]\n```\n\nAfter removing outliers, the data distribution became much cleaner.\n\nMore importantly, the model began learning actual patterns instead of noise.\n\nMachine Learning algorithms cannot understand text.\n\nThey only understand numbers.\n\nSo columns like:\n\n```\nMale\nFemale\n\nPrivate\nPublic\n\nGraduate\nMasters\n```\n\nneeded transformation.\n\nI applied One-Hot Encoding.\n\n```\npd.get_dummies(df,\n               columns=[\"gender\",\n                        \"company_type\"])\n```\n\nand Ordinal Encoding where order mattered.\n\n```\neducation_level\n\nHigh School\nGraduate\nMasters\nPhD\n```\n\nThis converted human-readable categories into machine-readable information.\n\nSome columns ranged between:\n\n```\n0 – 5\n```\n\nwhile others ranged between:\n\n```\n0 – 100000\n```\n\nDistance-based algorithms become biased toward larger values.\n\nI applied MinMax Scaling.\n\n``` python\nfrom sklearn.preprocessing import MinMaxScaler\n\nscaler = MinMaxScaler()\n\nX_train = scaler.fit_transform(X_train)\nX_test = scaler.transform(X_test)\n```\n\nNow every feature contributed fairly.\n\nI trained the exact same Logistic Regression model again.\n\nNothing changed except the data.\n\nResults:\n\n```\nBefore Feature Engineering : 72%\n\nAfter Feature Engineering  : 86%\n```\n\nA gain of 14 percentage points.\n\nWithout changing the algorithm.\n\nWithout using deep learning.\n\nWithout adding complexity.\n\nJust by improving the data.\n\nThis project changed the way I think about Machine Learning.\n\nEarlier I believed:\n\n```\nBetter Algorithm\n       ↓\nBetter Results\n```\n\nNow I believe:\n\n```\nBetter Data\n       ↓\nBetter Features\n       ↓\nBetter Results\n```\n\nMost real-world machine learning problems are not algorithm problems.\n\nThey are data problems.\n\nA powerful model trained on poor-quality data will still struggle.\n\nA simple model trained on clean, meaningful data can often outperform much more complex alternatives.\n\nThe hardest part was not training the model.\n\nThe hardest part was preparing the data.\n\nSome difficulties included:\n\nThese challenges taught me more than model training ever did.\n\nFeature Engineering is not the most glamorous part of Machine Learning.\n\nNobody posts screenshots of missing value treatment on social media.\n\nNobody celebrates scaling features.\n\nYet this is where much of the real improvement happens.\n\nAfter this project, I stopped asking:\n\nWhich model should I use?\n\nand started asking:\n\nWhat is my data trying to tell me?\n\nThat single change in mindset improved my machine learning skills more than learning any new algorithm.", "url": "https://wpnews.pro/news/better-data-beats-better-algorithms-before-changing-the-model-change-the-data", "canonical_source": "https://dev.to/vineet_chauhan_a828338181/better-data-beats-better-algorithms-before-changing-the-model-change-the-data-107k", "published_at": "2026-06-06 19:47:54+00:00", "updated_at": "2026-06-06 20:11:53.431560+00:00", "lang": "en", "topics": ["machine-learning"], "entities": ["Logistic Regression", "Decision Trees", "Random Forest", "XGBoost", "Neural Networks"], "alternates": {"html": "https://wpnews.pro/news/better-data-beats-better-algorithms-before-changing-the-model-change-the-data", "markdown": "https://wpnews.pro/news/better-data-beats-better-algorithms-before-changing-the-model-change-the-data.md", "text": "https://wpnews.pro/news/better-data-beats-better-algorithms-before-changing-the-model-change-the-data.txt", "jsonld": "https://wpnews.pro/news/better-data-beats-better-algorithms-before-changing-the-model-change-the-data.jsonld"}}