quicklearnkit 0.3.2

Learning-first machine learning utilities library for simplified imports, sampling, splitting, and probabilistic preprocessing.

QuickLearnKit

QuickLearnKit is a learning-first machine learning utilities library designed to make common ML and data science workflows simple, readable, and beginner-friendly — without blocking advanced users from full customization.

The philosophy is:

Remove mechanical friction so students can focus on concepts, not syntax.

QuickLearnKit provides:

• Easy model imports
• Random sampling utilities
• Train–test splitting
• Probabilistic, group-aware imputation
• Teaching-friendly plotting with optional value labels

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Installation

pip install quicklearnkit

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Quick Start

from quicklearnkit import (
    Sampler,
    train_test_split,
    ProbabilisticImputer,
    bar_plot
)

import seaborn as sns

# Load example dataset
df = sns.load_dataset("titanic")

# Sample data
sampler = Sampler(df, n=5, random_state=42)
print(sampler.sample())

# Split data
train, test = train_test_split(df, test_size=0.25)

# Impute missing values
imputer = ProbabilisticImputer("pclass", "deck", random_state=42)
df_imputed = imputer.fit_transform(df)

# Plot
bar_plot(df, x="class", y="fare", show_values="yes")

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Model Imports

QuickLearnKit allows you to import commonly used machine learning models without navigating deep module paths.

Example

from quicklearnkit import (
    LinearRegressionmodel,
    RandomForestRegressionmodel,
    XGBoostRegressionmodel,
    KNeighborsClassifiermodel,
    GradientBoostingClassifiermodel
)

lr_model = LinearRegressionmodel()
rf_model = RandomForestRegressionmodel()
xgb_model = XGBoostRegressionmodel()

knn_classifier = KNeighborsClassifiermodel()
gb_classifier = GradientBoostingClassifiermodel()

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Supported Models

Regression Models

• LinearRegressionmodel()
• KNNRegressionmodel()
• DecisionTreeRegressionmodel()
• RandomForestRegressionmodel()
• GradientBoostingRegressionmodel()
• AdaBoostRegressionmodel()
• XGBoostRegressionmodel()
• ElasticNetRegressionmodel()

Classification Models

• LogisticRegressionmodel()
• KNeighborsClassifiermodel()
• DecisionTreeClassifiermodel()
• RandomForestClassifiermodel()
• AdaBoostClassifiermodel()
• GradientBoostingClassifiermodel()
• XGBClassifiermodel()
• SVClassifiermodel()

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Utilities

1. Sampler

The Sampler class allows you to randomly select elements from:

• Python lists
• NumPy arrays
• pandas DataFrames

It supports both stateless (reproducible) and stateful (streaming/simulation) modes.

Initialization

Sampler(data, n=1, random_state=None, replace=False, axis=0, stateful=False)

Parameters Explained

Parameter	Type	Description
data	list, numpy.ndarray, pandas.DataFrame	The dataset to sample from
n	int	Number of samples to return
random_state	int or None	Seed for reproducibility. Same seed = same result
replace	bool	If True, sampling is done with replacement (duplicates allowed). If False, no duplicates
axis	int	Only applies to DataFrames. 0 = sample rows, 1 = sample columns
stateful	bool	If True, RNG state continues across calls. If False, sampling is reproducible on every call

Example

from quicklearnkit import Sampler
import seaborn as sns

df = sns.load_dataset("tips")

sampler = Sampler(df, n=3, random_state=42, replace=False)

sample1 = sampler.sample()
sample2 = sampler.sample()  # Same output if stateful=False

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2. Train–Test Split

Split datasets into training and testing sets with support for:

• Shuffling
• Stratification
• NumPy arrays
• pandas DataFrames

Function

train_test_split(data, test_size=0.25, shuffle=True, stratify=None, random_state=None)

Parameters Explained

Parameter	Type	Description
data	array-like or pandas.DataFrame	Dataset to split
test_size	float	Proportion of data to use as test set (e.g., 0.25 = 25%)
shuffle	bool	If True, data is shuffled before splitting
stratify	array-like or str	Column or labels to preserve class distribution in splits
random_state	int or None	Seed for reproducibility

Example

from quicklearnkit import train_test_split

train, test = train_test_split(df, test_size=0.3, shuffle=True, random_state=42)

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3. ProbabilisticImputer

A group-aware, probabilistic categorical imputer. It learns probability distributions from observed data and fills missing values by sampling from those distributions.

This allows:

• Realistic missing data handling
• Teaching probability-based imputation
• Reproducible preprocessing

Initialization

ProbabilisticImputer(group_col, target_col, random_state=None, stateful=False)

Parameters Explained

Parameter	Type	Description
group_col	str	Column used to group data (e.g., class, category)
target_col	str	Column where missing values will be imputed
random_state	int or None	Seed for reproducibility
stateful	bool	If True, RNG state advances across calls (useful for simulation). If False, output is reproducible

Methods

Method	Description
fit(df)	Learns probability distributions from known values
transform(df)	Imputes missing values using learned distributions
fit_transform(df)	Fit and transform in one step

Example

from quicklearnkit import ProbabilisticImputer
import seaborn as sns

df = sns.load_dataset("titanic")

imputer = ProbabilisticImputer(
    group_col="pclass",
    target_col="deck",
    random_state=42
)

imputed_df = imputer.fit_transform(df)

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Plotting (Teaching-Friendly Visualization)

QuickLearnKit provides wrappers around seaborn + matplotlib that allow students to display values on plots using a simple switch:

show_values="yes"

All plotting functions:

• Return a matplotlib Axes object
• Allow full customization (labels, limits, grids, styles)
• Automatically display the plot by default

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Common Parameters (All Plot Functions)

Parameter	Type	Description
data	pandas.DataFrame	Dataset used for plotting
x	str	Column for x-axis
y	str	Column for y-axis (if applicable)
title	str or None	Plot title
show_values	str	"yes" or "no" — whether to display numeric values
fmt	str	Format string for value labels (e.g. {:.2f})
show	bool	If True, displays plot immediately. If False, returns Axes for customization

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bar_plot

bar_plot(data, x, y, title=None, show_values="no", fmt="{:.1f}", show=True)

Example

from quicklearnkit import bar_plot
import seaborn as sns

_df = sns.load_dataset("tips")

ax = bar_plot(
    _df,
    x="day",
    y="total_bill",
    show_values="yes",
    show=False
)

ax.set_xlabel("Day of Week")
ax.set_ylabel("Average Bill")
ax.set_ylim(0, 40)

import matplotlib.pyplot as plt
plt.show()

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line_plot

line_plot(data, x, y, title=None, show_values="no", fmt="{:.2f}", show=True)

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scatter_plot

scatter_plot(data, x, y, title=None, show_values="no", fmt="{:.2f}", show=True)

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count_plot

count_plot(data, x, title=None, show_values="no", show=True)

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box_plot

Displays mean values when show_values="yes".

box_plot(data, x=None, y=None, title=None, show_values="no", fmt="{:.2f}", show=True)

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hist_plot

Displays bin counts when show_values="yes".

hist_plot(data, x, bins=10, title=None, show_values="no", fmt="{:.0f}", show=True)

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Random Data Generation

Generate random numerical arrays for experiments and demonstrations.

from quicklearnkit import create_random

random_data = create_random(mean=0, std_dev=1, size=100)

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Design Philosophy

QuickLearnKit is designed for:

• Students learning ML and data science
• Educators teaching concepts, not boilerplate
• Practitioners who want fast, clean workflows

It balances:

Beginner simplicity + Advanced control

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Contributing

Want to improve QuickLearnKit?

1. Fork the repository
2. Create a feature branch
3. Add tests and documentation
4. Submit a pull request

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License

MIT License

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QuickLearnKit helps you move from learning to building faster — without sacrificing clarity or control. 🚀