Data Science

ABSTRACT
Agriculture is first and foremost factor which is important for survival. Machine learning (ML) could
be a crucial perspective for acquiring real-world and operative solution for crop yield issue.
Considering the present system including manual counting, climate smart pest management and
satellite imagery, the result obtained arent really accurate. This paper focuses mainly on predicting
the yield of the crop by applying various machine learning techniques. The classifier models used
here include Logistic Regression, Naà ̄ve Bayes and Random Forest, out of which the Random
Forest provides maximum accuracy. The prediction made by machine learning algorithms will help
the farmers to come to a decision which crop to grow to induce the most yield by considering factors
like temperature, rainfall, area, etc. This bridges the gap between technology and agriculture sector.
Predicting crop yield based on the environmental, soil, water and crop parameters has been a
potential research topic. Deep-learning-based models are broadly used to extract significant crop
features for prediction. Though these methods could resolve the yield prediction problem there exist
the following inadequacies: Unable to create a direct non- linear or linear mapping between the raw
data and crop yield values; and the performance of those models highly relies on the quality of the
extracted features.
KEYWORDS Crop_yield_prediction, Logistic_regression, Naà ̄ve bayes,Random forest,
Weather_api, Climate change, deep learning, Precision algorithm

The process of extracting information to identify patterns, trends, and useful data
that would allow the business to take the data-driven decision from huge sets of data is called
Data Mining. Data Mining is the process of investigating hidden patterns of information to
various perspectives for categorization into useful data, which is collected and assembled in
particular areas such as data warehouses, efficient analysis, data mining algorithm, helping
decision making and other data requirement to eventually cost-cutting and generating revenue.
Data mining is the act of automatically searching for large stores of information to find trends
and patterns that go beyond simple analysis procedures. Data mining utilizes complex
mathematical algorithms for data segments and evaluates the probability of future events. Data
Mining is also called Knowledge Discovery of Data (KDD).This process includes various types
of services such as text mining, web mining, audio and video mining, pictorial data mining, and
social media mining. The biggest challenge is to analyze the data to extract important
information that can be used to solve a problem or for company development. There are many
powerful instruments and techniques available to mine data and find better insight from it.
Keywords
 Data mining
 Data warehouse
 Pattern
 Data cleaning
 Knowledge extraction
 Transformation
 Evaluation