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set -g mouse on
set -g history-limit 100000

set -g base-index 1
setw -g pane-base-index 1

unbind C-b
unbind C-a
set -g prefix C-Space
bind C-Space send-prefix

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Privacy Policy — Tadabbur Chrome Extension Last updated: 25 February 2026

Tadabbur is a Chrome new tab extension for Quranic reflection.

DATA COLLECTION

Tadabbur does not collect, transmit, or store any personal data. No analytics, tracking, or telemetry of any kind is used.

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TickTick Migration Weekend Checklist

Complete this checklist over one weekend to migrate from your current system (Google Calendar, scattered notes, etc.) to TickTick.

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Saturday Morning (2 hours)

Hour 1: Choose and Set Up

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Monthly Backlog Review - TickTick Task Setup

Create this recurring task in TickTick to ensure you review your exploration backlog regularly.

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Task Details

Task name: Monthly backlog review

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Exploration Backlog

Last reviewed: [Date]

Purpose: Store aspirational learning, tools to try, and project ideas outside your task manager. Review monthly to pull actionable items into TickTick.

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Tools to Try

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TickTick 5-List System Setup

Create exactly these 5 lists in TickTick:

1. Inbox

• Purpose: Default capture point
• Usage: Everything goes here first, no thinking required
• When to use: Quick capture from phone or desktop

2. Work – Client

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TransactionAmount	TransactionTime	MerchantInfo	LocationInfo	UserInfo	DeviceInfo
68.2	01/03/2023 14:30	LocalShop3	CityA	JohnDoe123	Mobile
95.4	02/03/2023 9:45	LocalShop2	CityB	AliceSmith456	Desktop
110.8	03/03/2023 16:15	OnlineStore1	CityC	BobJohnson789	Tablet
45.6	04/03/2023 12:00	OnlineStore1	CityA	EveWilliams123	Mobile
75.3	05/03/2023 10:30	OnlineStore2	CityB	CharlieBrown456	Desktop

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"""
This Python script showcases the application of decision trees in the context of e-commerce data analysis. 
"""

# Import necessary libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.tree import export_text

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"""
This Python script demonstrates the usage of logistic regression to predict whether customers will make the next purchase on an e-commerce site. 
The code performs the following steps:

1. Load and Preprocess Data:
    - Loads an e-commerce dataset containing customer features such as 'time_on_site', 'total_spent', 'is_returning_customer', and 'will_make_next_purchase'.
    - Splits the data into training and testing sets.

2. Model Training:
    - Creates a logistic regression model using scikit-learn.

kashaziz

"""
This script utilizes machine learning techniques to predict house prices based on various input features.
It performs data preprocessing, splits the data into training and testing sets, trains a linear regression model,
evaluates its performance using  mean squared error (MSE) and R-squared.
"""

import pandas as pd
import numpy as np
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import train_test_split