Better Data Beats Better Algorithms: Before Changing the Model, Change the Data

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.

How Feature Engineering Taught Me That Better Data Often Beats Better Algorithms When I first started learning Machine Learning, I believed what many beginners believe: If my model is not performing well, I need a better algorithm. So I kept switching models. I moved from Logistic Regression to Decision Trees, then Random Forest, and later even started reading about XGBoost and Neural Networks. The results improved slightly, but never dramatically. What surprised me was that the biggest improvement didn't come from changing the algorithm. It came from changing the data. I was working on a dataset containing missing values, outliers, and categorical variables. Like many beginners, my first instinct was simple: model.fit X train, y train pred = model.predict X test The model trained successfully. The accuracy looked acceptable. But something felt wrong. The data itself was messy. Some columns contained missing values. Some numerical features had extreme outliers. Several categorical columns were represented as text. Yet I expected the model to magically learn everything. I trained a Logistic Regression model on the raw dataset. Results: Accuracy : 72% Not terrible. Not impressive either. Instead of changing the model, I decided to investigate the data. This turned out to be the most important decision of the entire project. The dataset contained several missing values. At first I considered simply deleting rows. df.dropna inplace=True The problem? I lost a significant portion of the data. So I experimented with multiple approaches: python from sklearn.impute import SimpleImputer imputer = SimpleImputer strategy='mean' X = imputer.fit transform X imputer = SimpleImputer strategy='median' python from sklearn.impute import KNNImputer imputer = KNNImputer n neighbors=5 X = imputer.fit transform X KNN preserved relationships between records much better than simple averaging. This alone improved performance. I then visualized the numerical columns. The boxplots looked terrible. A few extreme values were stretching entire distributions. sns.boxplot df "experience" The model was spending too much effort trying to fit a handful of unusual observations. I used IQR-based treatment. Q1 = df "experience" .quantile 0.25 Q3 = df "experience" .quantile 0.75 IQR = Q3 - Q1 lower = Q1 - 1.5 IQR upper = Q3 + 1.5 IQR df = df df "experience" = lower & df "experience" <= upper After removing outliers, the data distribution became much cleaner. More importantly, the model began learning actual patterns instead of noise. Machine Learning algorithms cannot understand text. They only understand numbers. So columns like: Male Female Private Public Graduate Masters needed transformation. I applied One-Hot Encoding. pd.get dummies df, columns= "gender", "company type" and Ordinal Encoding where order mattered. education level High School Graduate Masters PhD This converted human-readable categories into machine-readable information. Some columns ranged between: 0 – 5 while others ranged between: 0 – 100000 Distance-based algorithms become biased toward larger values. I applied MinMax Scaling. python from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler X train = scaler.fit transform X train X test = scaler.transform X test Now every feature contributed fairly. I trained the exact same Logistic Regression model again. Nothing changed except the data. Results: Before Feature Engineering : 72% After Feature Engineering : 86% A gain of 14 percentage points. Without changing the algorithm. Without using deep learning. Without adding complexity. Just by improving the data. This project changed the way I think about Machine Learning. Earlier I believed: Better Algorithm ↓ Better Results Now I believe: Better Data ↓ Better Features ↓ Better Results Most real-world machine learning problems are not algorithm problems. They are data problems. A powerful model trained on poor-quality data will still struggle. A simple model trained on clean, meaningful data can often outperform much more complex alternatives. The hardest part was not training the model. The hardest part was preparing the data. Some difficulties included: These challenges taught me more than model training ever did. Feature Engineering is not the most glamorous part of Machine Learning. Nobody posts screenshots of missing value treatment on social media. Nobody celebrates scaling features. Yet this is where much of the real improvement happens. After this project, I stopped asking: Which model should I use? and started asking: What is my data trying to tell me? That single change in mindset improved my machine learning skills more than learning any new algorithm.