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Data mining assignment help is one of the most sought-after services. It is a challenging task to discover patterns in large data sets. A student might spend nights solving data mining assignments but they still cannot come up with an accurate solution. Data mining is the most difficult and challenging subject for students and writing assignments on different concepts of data mining would be stressful and despairing for students who are already loaded with other assignments.
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Data mining is a sophisticated tool that helps you to extract data from huge chunks of data available right there. This also helps to find relationships with different data sets. The data is evaluated using statistical models, artificial intelligence techniques, mathematical algorithms, and machine learning methods that will boost the accuracy and performance of these data analysis tools.
Data mining majorly focuses on collecting, managing, and analyzing data to predict future markets or results. It helps the companies acquire information about their customers along with the behavior from the treasure troves of data. The data is thoroughly analyzed from different perspectives and is presented in various forms, including text, multimedia, and quantitative forms. This computing process is used by many companies to extract useful information from raw data. Data mining assignment is given to the students to ensure they are job ready after their computer science education.
A data mining software will analyze the data that is stored in various patterns and then find out the relationships between them based on the open-ended queries by the users. There are three types of data mining analytical software. They include – Statistical software, Machine learning and the third one are neural networks. These three types are used to establish the relationship between the following:
Please find below 4 important steps in data mining
Now you understand that the students should write the assignments after cleaning, clustering and analyzing the data and extracting the right information in the form of patterns. It is a nerve-racking academic task for IT students to write a data mining assignment as they have to choose the topic, draft the assignment, edit and proofread the content. To get rid of this stressful process, you can seek the help of our Data Mining Homework Help experts.
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OLAP Operations | Correlation analysis |
Clustering and classification of analytical procedures | Parameter optimization |
Dimension reduction | Data cleaning |
Data mining process | Data transformation |
Bioinformatics and proteomics | Data mining knowledge representation |
Decision Tree | Data Visualization |
Computing and Data Analysis | Discretization |
WEKA 3D Data Mining | Detection of Outliers |
Item set Mining | Sequence Mining |
Graph Pattern Mining | Hierarchical Clustering |
Probability Classification | Data Warehouse Modeling |
Data Cube Technology | Advanced Pattern Mining |
Cluster Analysis | Outlier Detection |
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Code for: Data Preprocessing in Python
Solution:
import pandas as pd
from IPython.display import display
import warnings
warnings.simplefilter("ignore")
lst = ["2017", "2018", "2019", "2020", "2021"]
data = pd.DataFrame(columns=["Date","ASSAULT", "BATTERY", "BURGLARY", "HOMICIDE", "ROBBERY", "THEFT"])
for year in lst:
df = pd.read_csv("/content/drive/MyDrive/Colab Notebooks/Chicago Crimes - {}.csv".format(year))
# Sort by Date of Crime
df = df.sort_values("Date")
# Keep only those records that are one of the following primary type "THEFT", "ASSAULT", "HOMICIDE", "BURGLARY", "ROBBERY", "BATTERY"
df = df.loc[df["Primary Type"].isin(["THEFT", "ASSAULT", "HOMICIDE", "BURGLARY", "ROBBERY", "BATTERY"])]
#Convert the DateTime stamp to the following format: YYYYMMDD
df["Date"] = df["Date"].str.split(" ",expand=True)[0]
df["Date"] = pd.to_datetime(df["Date"],dayfirst=True)
df["Date"] = df["Date"].astype(str)
df["Date"] = df["Date"].str.replace("-","")
#Convert the data to time-series data
df = pd.concat([df, pd.get_dummies(df['Primary Type'])], axis=1)
df = df.groupby(["Date"],as_index=False)["ASSAULT", "BATTERY", "BURGLARY", "HOMICIDE", "ROBBERY", "THEFT"].sum()
print("After preprocessing our {} year dataframe has {} rows." .format(year, df.shape[0]))
#save the dataframe to csv
df.to_csv("{}_output.csv".format(year))
display(df.head())
# concating all the five dataframe
data = data.append(df)
print()
print("After concating all the five dataframes we have {} rows." .format(data.shape[0]))
#sort by date
data = data.sort_values("Date")
# save the dataframe
data.to_csv("combined_data.csv")
data.head()
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