{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# INTRODUCTION\n \nTabular data include customer information in a sales database, stock market prices in a financial dataset, or student grades in an academic record.\n \n \nTime series data refers to a sequence of data points collected over regular intervals of time. This data type is used to analyse trends, patterns, and behaviour that evolve over time. Each data point in a time series is associated with a specific timestamp or time period. Time series data is often used for forecasting, trend analysis, and understanding the dynamics of a particular phenomenon. Examples of time series data include stock market prices recorded every minute, monthly sales figures over several years, or temperature measurements taken every hour.\n\nTabular data is structured information organized in rows and columns, while time series data is a sequence of data points collected over time. Both types of data are widely used in various applications and can provide valuable insights when analysed effectively.\nrefers to information organized in a tabular format, often resembling a spreadsheet or a database table. Each row in the table represents a specific record or observation, while each column represents a different attribute or feature of that record. Tabular data is structured and well-defined, making it easy to understand and manipulate. ","metadata":{}},{"cell_type":"markdown","source":"# HISTORY OF TABULAR📈 AND TIME SERIES DATA 📉💡⏱\n\nTabular Data:\n\nAncient Times: Humans have been using tabular representations for organizing information for centuries. One of the earliest known examples is the ancient Babylonian cuneiform tablets, which documented various economic and administrative transactions in a tabular format.\n\n20th Century: With the advent of computers, tabular data became more prevalent. In the mid-20th century, the development of database systems and spreadsheet software facilitated the efficient storage and manipulation of tabular data. The introduction of the Relational Database Management System (RDBMS) concept by Edgar F. Codd in the 1970s further revolutionized the management of structured data.\n\nTime Series Data:\n\nEarly Observations: Humans have been recording and analyzing time-related data for centuries. Astronomers, for example, have been observing celestial events and tracking the positions of celestial bodies over time. This led to the development of rudimentary time series data.\n\n19th Century: The Industrial Revolution marked a significant milestone in the collection and analysis of time series data. With the advancement of technology and machinery, industries started recording various measurements such as temperature, production output, and economic indicators over time. Time series analysis techniques began to emerge, such as moving averages and trend analysis, to understand patterns and make predictions.\n\n20th Century: The widespread use of computers in the latter half of the 20th century facilitated the handling and analysis of large-scale time series data. Statistical methods like autoregressive models, exponential smoothing, and spectral analysis gained popularity. Time series data found applications in diverse fields, including finance, economics, meteorology, and social sciences.\n\nPresent: In recent years, the advent of big data and the Internet of Things (IoT) has led to an explosion of time series data. Sensors, devices, and online platforms generate massive amounts of time-stamped data, enabling real-time monitoring, predictive analytics, and improved decision-making.\n","metadata":{}},{"cell_type":"markdown","source":"# 2023 Kaggle AI Report on Tabular📈 and Time Series Data⏱💡\n\nThe 2023 Kaggle AI Report offers valuable insights into the utilization of tabular and time series data in the field of artificial intelligence. This report highlights the latest trends, techniques, and applications related to these data types, providing a comprehensive understanding of their significance in various domains.\n\nGrowing Importance of Tabular Data:\nThe report emphasizes the continued significance of tabular data in AI applications. With the abundance of structured information in business, finance, and research domains, tabular data remains a crucial resource for decision-making and predictive modeling. The report highlights advancements in handling large-scale tabular datasets and optimizing algorithms for improved performance.\n\nEvolution of Time Series Analysis:\nThe 2023 report sheds light on the evolution of time series analysis techniques. It showcases innovative approaches and algorithms that have emerged in recent years, enabling better forecasting, anomaly detection, and understanding of temporal patterns. The report highlights the integration of deep learning and recurrent neural networks (RNNs) for more accurate and robust time series modeling.\n\nIntegration of Tabular and Time Series Data:\nOne significant observation made in the report is the increasing synergy between tabular and time series data analysis. The combination of these data types offers enhanced insights and predictive power. The report showcases case studies where tabular and time series data are leveraged together to develop comprehensive AI models in domains like finance, healthcare, and energy.\n\nImportance of Feature Engineering:\nThe report underscores the continued importance of feature engineering in both tabular and time series data analysis. Feature selection, transformation, and creation techniques significantly impact the performance of machine learning models. The report highlights advancements in automated feature engineering approaches and provides insights into best practices for extracting meaningful features from tabular and time series datasets.\n\nThe 2023 Kaggle AI Report provides valuable insights into the current state and future directions of tabular and time series data analysis. It emphasizes the growing significance of these data types, showcases emerging techniques, and highlights their integration for more comprehensive AI models. Researchers, practitioners, and data scientists can leverage the findings of this report to enhance their understanding and improve the performance of their AI applications involving tabular and time series data.\n","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{}}]}