nto a pandas dataframe named data_firstname where first name is you name. 6. Using Pandas, Matplotlib, seaborn (you can use any or a mix) generate 3-5 plots and add them to your written response explaining what are the key insights and findings from the plots. 7. Separate the features from the class. 8. Split your data into train 80% train and 20% test, use the last two digits of your student number for the seed. Build Classification Models Support vector machine classifier with linear kernel breast cancer problem : I have already answered 1 to 3. Please provide solution from 4,5,6,7. Programming language python

nto a pandas dataframe named data_firstname where first name is you name. 6. Using Pandas, Matplotlib, seaborn (you can use any or a mix) generate 3-5 plots and add them to your written response explaining what are the key insights and findings from the plots. 7. Separate the features from the class. 8. Split your data into train 80% train and 20% test, use the last two digits of your student number for the seed. Build Classification Models Support vector machine classifier with linear kernel breast cancer problem : I have already answered 1 to 3. Please provide solution from 4,5,6,7. Programming language python

Oracle 12c: SQL

3rd Edition

ISBN:9781305251038

Author:Joan Casteel

Publisher:Joan Casteel

Chapter3: Table Creation And Management

Section: Chapter Questions

Problem 9RQ: What happens if you try to decrease the scale or precision of a NUMBER column to a value less than...

See similar textbooks

Similar questions

Prestige data is data set in the package carData in R. Download and install thispackage and use it to perform the following exercises.1. (a) How can you show the structure of the data? What data structure is prestige andwhat data type is each variable in Prestige?(b) What are the dimensions of the data?(c) Display the first six and last six rows of the data(d) What is the highest and the lowest Income? which category of occupation(s)is(are) paid these incomes?2. What percentage of occupational incumbents earn more than $25,000?3. What percentage of occupational incumbents earn above $20,000 and below $10,000?4. Construct a box plot to compare the distribution of Income according to type of occupation. Comment on the distribution of each box plot and the presence of outliers.Colour the box plots with different colours OF your choice.5. Construct a histogram for each for income of each type of occupation. Do your histograms conform to your results from the box plots?6. Select the data on…
For this question please paste in a single line of code that loads training and test data for the keras fashion mnist dataset.
The Driver Relationship team wants to analyze drivers and their car usages in InstantRide for the month October. Thus, they want to focus on each driver and respective car allocation occurred. In more detail, the team requires the DRIVER_ID and the CAR_ID with their usage counts as the TOTAL column for the travels occurred in the month of October. Since the team wants to also learn the total number of the travels by the drivers you need to use GROUP BY ROLLUP functionality.
Create ERD for Real-estate database: There are Many Cities in each State There are Many Zip Codes in each City Many Houses belong to each ZipCode Each House can be one of HouseTypes ( Town House, Residential, Condo, Single Family) Each House can be Sold multiple times. ( entity Sales
Scenario: You are required to create the Data Flow Diagram for a website for "Online motorcycles in Nizwa purchase system". This system will be used by customers who want to buy any bike models from Nizwa. Customers can search bike models using search bar option. Customers must register their detail and open an account on the website to make any purchase. Customers can choose to make online payment (via debit card) or pay cash on delivery. If customer chooses to make online payment, system will provide the Payment Receipt and Order ID. Website administrator can add/delete/update items on website. Level-0: must include possible external entities, process and data flows. A BI !!
Use the iloc() function to extract the first 20 features of the dataframe har_train. Save this new dataframe to first_twenty. Next, using the seaborn library create a heatmap for the correlation matrix. First you have to create the correlation matrix from the pandas dataframe (save it in a dataframe called corr) and then plot it using seaborn with these customizations: Set the seaborn style to white. Generate a mask using np.triu(np.ones_like()) with the dtype as boolean to only show the lower triangle of the correlation matrix. Save it in a variable called mask. Set up the figure with matplotlib with figsize=(11,9). Use fig, ax = ... Generate a custom diverging colormap for the heatmap with the arguments (220, 10, as_cmap=True). Save it in a variable called cmap. Draw the heatmap with the mask and correct aspect ratio, using the arguments corr, mask=mask, cmap=cmap, vmax=.3, center=0, square=True, linewidths=.5, cbar_kws={"shrink": .5}). Finally, use fig.tight_layout() just before…
Prestige data is data set in the package carData in R. Download and install this package and use it to perform the following exercises. 1. (a) How can you show the structure of the data? What data structure is prestige and what data type is each variable in Prestige? (b) What are the dimensions of the data? (c) Display the first six and last six rows of the data (d) What is the highest and the lowest Income? which category of occupation(s) is(are) paid these incomes? 2. What percentage of occupational incumbents earn more than $25,000? 3. What percentage of occupational incumbents earn above $20,000 and below $10,000? 4. Construct a box plot to compare the distribution of Income according to type of oc- cupation. Comment on the distribution of each box plot and the presence of outliers. Colour the box plots with different colours OF your choice. 5. Construct a histogram for each for income of each type of occupation. Do your his- tograms conform to your results from the box plots? 6.…
You need to provide two files. 1. word/pdf report file 2. you code in *.sql/.txt file 1. word/pdf report file: 1st task: You have to choose a suitable topic. Many topics are available in the internet/other sources. It is better (not mandatory) if your topic is an unique one. 2nd task: Give a description of your topic. Don't put unnecessary description which is not required in generating the ER diagram. (for example: In University DB managament project, you may write that our university is a world class university etc.) 3rd task: You may identify your entities/relationships (not mandatory). Draw you ER diagram according to your description. 4th task: Draw your schema diagram from your ER diagram. It means you have now all the tables with constraints. 5th task: Create the database, tables/objects. Then insert some dummy data (10-20-40 rows). Provide the *.sql file seperately. 6th task: You have already studied the chapter-3 where you studied different query related topics. Apply your…
Load & check the data:1. Load the data into a pandas dataframe named data_firstname where first name is you name.2. Carryout some initial investigations:a. Check the names and types of columns.b. Check the missing values.c. Check the statistics of the numeric fields (mean, min, max, median, count..etc.)d. In you written response write a paragraph explaining your findings about each column. Pre-process and visualize the data3. Replace the ‘?’ mark in the ‘bare’ column by np.nan and change the type to ‘float’4. Fill any missing data with the median of the column.5. Drop the ID column6. Using Pandas, Matplotlib, seaborn (you can use any or a mix) generate 3-5 plots and add them to your written response explaining what are the key insights and findings from the plots.7. Separate the features from the class.8. Split your data into train 80% train and 20% test, use the last two digits of your student number for the seed. Build Classification ModelsSupport vector machine classifier with…
Load & check the data:1. Load the data into a pandas dataframe named data_firstname where first name is you name.2. Carryout some initial investigations:a. Check the names and types of columns.b. Check the missing values.c. Check the statistics of the numeric fields (mean, min, max, median, count..etc.)d. In you written response write a paragraph explaining your findings about each column.Pre-process and visualize the data3. Replace the ‘?’ mark in the ‘bare’ column by np.nan and change the type to ‘float’4. Fill any missing data with the median of the column.5. Drop the ID column6. Using Pandas, Matplotlib, seaborn (you can use any or a mix) generate 3-5 plots and add themto your written response explaining what are the key insights and findings from the plots.7. Separate the features from the class.8. Split your data into train 80% train and 20% test, use the last two digits of your student numberfor the seed.Build Classification ModelsSupport vector machine classifier with…
Load & check the data:1. Load the data into a pandas dataframe named data_firstname where first name is you name.2. Carryout some initial investigations:a. Check the names and types of columns.b. Check the missing values.c. Check the statistics of the numeric fields (mean, min, max, median, count..etc.)d. In you written response write a paragraph explaining your findings about each column.Pre-process and visualize the data3. Replace the ‘?’ mark in the ‘bare’ column by np.nan and change the type to ‘float’4. Fill any missing data with the median of the column.5. Drop the ID column6. Using Pandas, Matplotlib, seaborn (you can use any or a mix) generate 3-5 plots and add themto your written response explaining what are the key insights and findings from the plots.7. Separate the features from the class.8. Split your data into train 80% train and 20% test, use the last two digits of your student numberfor the seed.Build Classification ModelsSupport vector machine classifier with…
Load & check the data:1. Load the data into a pandas dataframe named data_firstname where first name is you name.2. Carryout some initial investigations:a. Check the names and types of columns.b. Check the missing values.c. Check the statistics of the numeric fields (mean, min, max, median, count..etc.)d. In you written response write a paragraph explaining your findings about each column.Pre-process and visualize the data3. Replace the ‘?’ mark in the ‘bare’ column by np.nan and change the type to ‘float’4. Fill any missing data with the median of the column.5. Drop the ID column6. Using Pandas, Matplotlib, seaborn (you can use any or a mix) generate 3-5 plots and add themto your written response explaining what are the key insights and findings from the plots.7. Separate the features from the class.8. Split your data into train 80% train and 20% test, use the last two digits of your student numberfor the seed.Build Classification ModelsSupport vector machine classifier with…