PREDICTION OF ADMISSION AND JOBS TRENDS IN THE FIELD OF ENGINEERING AND TECHNOLOGY, PHARMACY AND MANAGEMENT BASED ON THE DEMOGRAPHIC LOCATION

AICTE-approved colleges all throughout India provided the data for the academic years 2013–2021 (India's state–by–state admission and placement statistics). The course name, state, year, institution type, enrollment, placement, and graduation rates were among the twelve variables taken into account. After handling missing values, there were between 1700 and 2000 samples in the raw data set. Two indicators for prospective enrolment and placement levels in the upcoming school year were created by algorithms..
statistics on AICTE-approved colleges in Gujarat for management, engineering, and technology programs was gathered from 2015 to 2021 (Gujarat district-level statistics for admission and placement). Eight factors, including admissions and placements for both public and private colleges in each area, were included in the data. Two predictors for possible enrolment and placement levels for the following year were generated using algorithms. A group-by-group study of the original data set's 5000–5500 samples resulted in a reduction of 1700–1000 samples. To predict enrollment and placement for certain districts, courses, and branches, the data may be used.
statistics on government-approved startup firms in India were gathered (State-wise statistics for new startup companies) between 2016 and 2022, taking into account seven factors. A group-by-group study of the number of firms in each state and year decreased the data set's approximately 55,124 raw samples to an estimated 133,697. When projecting future company growth, the output offered information on the number of enterprises that had been founded in a certain industry and state.
Data on particular student information, including names, enrolment, and areas like engineering, technology, and textiles, were gathered from LD Institute and are included in the set of data for the institutional study. The appropriate company of placement and the graduation year were included in the data set.

本数据集包含四部分内容：
1. 全印度经全印度技术教育委员会（All India Council for Technical Education，AICTE）认证的院校2013-2021学年数据，涵盖印度各邦的招生与就业统计信息。本次分析共纳入12项变量，包括课程名称、所属邦份、统计年份、院校类型、招生人数、就业情况与毕业率等。经缺失值处理后，原始数据集包含1700至2000条样本。算法基于数据生成两项指标，用于预测下一学年的潜在招生规模与就业水平。
2. 2015-2021年印度古吉拉特邦内经AICTE认证的管理、工程与技术类院校数据，包含该邦各区县的招生与就业统计信息。该数据集共纳入8项指标，涵盖各区县公办与民办院校的招生与就业情况。算法同样生成两项预测指标，用于预判下一年度的潜在招生与就业规模。原始数据集初始包含5000至5500条样本，经分组分析后样本量缩减至1700至1000条。该数据集可用于预测特定区县、课程与专业方向的招生与就业情况。
3. 2016-2022年印度经官方认证的初创企业数据，包含各邦新增初创企业的统计信息，共纳入7项分析维度。通过按邦份与年份分组统计企业数量，原始数据集的约55124条样本经处理后缩减至约133697条。在预测未来企业发展态势时，该数据集可提供特定行业与邦份的新增企业数量相关信息。
4. 源自LD学院的学生数据，采集了特定学生的相关信息，包括姓名、招生信息以及所学专业方向（如工程、技术与纺织等），用于开展院校层面的研究。该数据集同时包含学生的签约就业单位与毕业年份信息。