Metadata-Version: 2.4
Name: smartanalytics
Version: 1.0.0
Summary: A Python library for data cleaning, statistical analysis, and insights.
Author: Madiha smartanalytics Team
License-Expression: MIT
Project-URL: Homepage, https://pypi.org/project/smartanalytics/
Keywords: data,cleaning,statistics,analytics,pandas,numpy,data science
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas>=1.3.0
Requires-Dist: numpy>=1.21.0
Dynamic: license-file

# smartanalytics

A Python library for **data cleaning**, **statistical analysis**, and **data insights**.

## Installation

```bash
pip install smartanalytics
```

## Modules

### 1. `cleaning` — Data Cleaning
Clean and preprocess your DataFrames before analysis.

| Function | Description |
|---|---|
| `remove_nulls(df)` | Remove rows with missing values |
| `remove_duplicates(df)` | Remove duplicate rows |
| `fill_missing(df, value)` | Fill NaN values with a given value |
| `normalize_data(df)` | Min-Max normalize all numeric columns |

### 2. `stats` — Statistics
Compute core statistical measures from lists.

| Function | Description |
|---|---|
| `mean(data)` | Arithmetic mean |
| `median(data)` | Middle value |
| `mode(data)` | Most frequent value(s) |
| `standard_deviation(data)` | Spread of the data |

### 3. `insights` — Data Insights
Advanced analysis functions for real-world data understanding.

| Function | Description |
|---|---|
| `detect_outliers(data)` | IQR-based outlier detection |
| `correlation_matrix(df)` | Pearson correlation between columns |
| `dataset_summary(df)` | Shape, dtypes, nulls, and stats overview |
| `missing_value_report(df)` | Column-wise missing value report |

## Quick Example

```python
import pandas as pd
from smartanalytics import cleaning, stats, insights

# Sample dataset
df = pd.DataFrame({
    'Age':    [25, 30, None, 22, 30],
    'Score':  [88, 92, 95, 88, 92],
    'Salary': [30000, 45000, 50000, 28000, 45000]
})

# Clean the data
df = cleaning.remove_nulls(df)
df = cleaning.remove_duplicates(df)

# Statistical analysis
print(stats.mean(df['Score'].tolist()))
print(stats.standard_deviation(df['Salary'].tolist()))

# Generate insights
print(insights.missing_value_report(df))
print(insights.correlation_matrix(df))
```

## License
MIT
