Demystifying_Feature_Engineering.ipynb

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        "# Demystifying Feature Engineering\n",
        "## The secret weapon for building powerful machine-learning models\n",
        "\n",
        "\n",
        "\n",
        "| ![space-1.jpg](https://github.com/Tanu-N-Prabhu/Python/blob/master/Img/ray-rui-SyzQ5aByJnE-unsplash.jpg?raw=true) |\n",
        "|:--:|\n",
        "| Photo by ray rui on Unsplash|\n",
        "\n",
        "\n",
        "### Why it&rsquo;s so important\n",
        "In machine learning, algorithms often get all the credit. But the real performance boost? That usually comes from feature engineering; transforming raw data into meaningful inputs.\n",
        "\n",
        "The quality of your features determines how well your models perform. You can throw a basic model at great features and still beat a complex model fed with garbage data.\n",
        "\n",
        "In this post, I will break down the core techniques of feature engineering using Python, with clear examples and clean implementation.\n",
        "\n",
        "---\n",
        "\n",
        "### Key Concepts Covered\n",
        "* Feature extraction: Turning unstructured data (e.g., text, timestamps) into usable form.\n",
        "* Feature transformation: Applying scaling, encoding, binning, and log transformations.\n",
        "* Feature selection: Dropping redundant or irrelevant features to reduce overfitting.\n",
        "* Handling categorical data: Label encoding vs. One-hot encoding.\n",
        "* Interaction features: Combining columns to capture hidden patterns.\n",
        "* Dealing with missing values: Imputation techniques that retain model integrity.\n",
        "\n",
        "---\n",
        "\n",
        "### Fun Fact\n",
        "Kaggle grandmasters often say: \"***80% of your model&rsquo;s performance comes from great features, not complex models!***\"\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "### Implementation\n"
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        "import pandas as pd\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.feature_selection import VarianceThreshold\n",
        "\n",
        "# Sample DataFrame\n",
        "df = pd.DataFrame({\n",
        "    'age': [25, 30, None, 45],\n",
        "    'city': ['NY', 'LA', 'NY', 'SF'],\n",
        "    'income': [50000, 60000, 55000, None],\n",
        "    'signup_date': ['2022-01-15', '2022-03-20', '2022-01-22', '2022-05-30']\n",
        "})\n",
        "\n",
        "# --------------------------\n",
        "# 1. Feature Extraction\n",
        "# --------------------------\n",
        "# Extracting month from signup_date\n",
        "df['signup_month'] = pd.to_datetime(df['signup_date']).dt.month\n",
        "df.drop('signup_date', axis=1, inplace=True)\n",
        "\n",
        "# --------------------------\n",
        "# 2. Handling Missing Values\n",
        "# --------------------------\n",
        "imputer = SimpleImputer(strategy='mean')\n",
        "df[['age', 'income']] = imputer.fit_transform(df[['age', 'income']])\n",
        "\n",
        "# --------------------------\n",
        "# 3. Feature Transformation\n",
        "# --------------------------\n",
        "# One-hot encode the 'city' column\n",
        "df = pd.get_dummies(df, columns=['city'])\n",
        "\n",
        "# Scale numerical columns\n",
        "scaler = StandardScaler()\n",
        "df[['age', 'income']] = scaler.fit_transform(df[['age', 'income']])\n",
        "\n",
        "# --------------------------\n",
        "# 4. Interaction Features\n",
        "# --------------------------\n",
        "# Create a new feature combining age and income\n",
        "df['age_income_interaction'] = df['age'] * df['income']\n",
        "\n",
        "# --------------------------\n",
        "# 5. Feature Selection\n",
        "# --------------------------\n",
        "# Remove features with low variance\n",
        "selector = VarianceThreshold(threshold=0.01)\n",
        "df_selected = pd.DataFrame(selector.fit_transform(df), columns=df.columns[selector.get_support()])\n",
        "\n",
        "# --------------------------\n",
        "# Final Output\n",
        "# --------------------------\n",
        "df_selected"
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              "        age    income  signup_month  city_LA  city_NY  city_SF  \\\n",
              "0 -1.132277 -1.414214           1.0      0.0      1.0      0.0   \n",
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              "summary": "{\n  \"name\": \"df_selected\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1547005383792515,\n        \"min\": -1.132277034144596,\n        \"max\": 1.5851878478024335,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -0.4529108136578386,\n          1.5851878478024335,\n          -1.132277034144596\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1547005383792517,\n        \"min\": -1.4142135623730951,\n        \"max\": 1.4142135623730951,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -1.4142135623730951,\n          1.4142135623730951,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"signup_month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9148542155126762,\n        \"min\": 1.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.0,\n          3.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city_LA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city_NY\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5773502691896257,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city_SF\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age_income_interaction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9563105743062319,\n        \"min\": -0.640512615220349,\n        \"max\": 1.6012815380508718,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.6012815380508718,\n          -0.640512615220349\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### What's Covered\n",
        "* Feature Extraction &rarr; `signup_month`\n",
        "* Missing Value Imputation &rarr; with `SimpleImputer`\n",
        "* Transformation &rarr; `StandardScaler`, `get_dummies`\n",
        "* Interaction Feature &rarr; `age * income`\n",
        "* Feature Selection &rarr; with `VarianceThreshold`\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "> ***This is the foundation of every high-performance machine learning pipeline. It&rsquo;s not about fancy models, it&rsquo;s about feeding them the right data.***\n",
        "\n",
        "---\n",
        "\n",
        "### Conclusion\n",
        "Feature engineering isn&rsquo;t just about cleaning data, it&rsquo;s about unlocking insights. The right transformations can turn raw numbers into gold. Master these steps and you&rsquo;re already ahead of the curve! Thanks for reading my article, let me know if you have any suggestions or similar implementations via the comment section. Until then, see you next time. Happy coding!\n",
        "\n",
        "---\n",
        "\n",
        "### Before you go\n",
        "* Be sure to **Like** and **Connect** Me&#65039;&#65039;\n",
        "* Follow Me : [Medium](https://medium.com/@tanunprabhu95) | [GitHub](https://github.com/Tanu-N-Prabhu) | [LinkedIn](https://ca.linkedin.com/in/tanu-nanda-prabhu-a15a091b5) | [Python Hub](https://github.com/Tanu-N-Prabhu/Python)\n",
        "* [Check out my latest articles on Programming](https://medium.com/@tanunprabhu95)\n",
        "* Check out my [GitHub](https://github.com/Tanu-N-Prabhu) for code and [Medium](https://medium.com/@tanunprabhu95) for deep dives!\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "SU1zR_coPfIa"
      }
    }
  ]
}