California Home Price Prediction Using Machine Learning

Machine Learning Modeling for Predicting Home Prices in California

1. DS3 Model
Presentation
Xiao Xiao, Mark Miao, Xuling Yang, Celena
Kim, Ayush Gupta

2. Meet Team DS3
Team Lead: Xiao Xiao
Columbia University - Graduate

Mark Miao
University of Pennsylvania-
Graduate
Xuling Yang
University of Michigan - Graduate

Celena Kim
Northwestern University - Undergraduate

Ayush Gupta
Rutgers University - Undergraduate

3. Contents of this
Presentation
Project Introduction
Collective Data Prep & Rationale
Our Models
Results Summary
Future Steps

4. Project
Overview
01

5. Project Overview
OBJECTIVE DATA SOURCE
TECHNOLOGIES COLLABORATION
OUTCOME
Created robust
predictive models that
provide valuable
insights for real estate
price estimation.
Develop machine learning models to
predict the close price of real estate
properties
Datasets containing historical data of
properties from the California
Regional Multiple Listing Service
(CRMLS)
Python and its machine learning
libraries
Worked collaboratively to resolve
errors and improve model accuracy

6. Project timeline
Data preparation round 1 Model Selection Initial Model Fitting Data preparation round 2
Model Tuning Best Model Error
Analysis
Next Stage Planning

7. 02
Data Prep

8. Data Prep and Rationale
●Intro to original data

●Variable categorizing
●Handling sparse information

●Capturing yearly increasing
house price

●Transforming variable to be
more directly related with
house price
●Outlier cleaning

●Dropping/filling missing value

●Removing invalid values

Variable Selection Feature Engineering Data Cleaning

9. 1.Listing Related Variables

2.Unknown In Use Case Variable

3.Database Management Related Variables

4.Property Related Variables
•Location Related
•Property Attributes
•Neighborhood

Variable Selection
Large Number of Variables
4,149,768
rows
82
variables
Variable Categorization
Variable Category Variables Included
Listing Related
ListAgentEmail, ListAgentFirstName, ListAgentLastName, ListOfficeName,
BuyerOfficeName, CoListOfficeName, CoListAgentFirstName,
CoListAgentLastName, BuyerAgentFirstName, BuyerAgentLastName,
BuyerOfficeAOR, BuyerAgencyCompensationType, BuyerAgencyCompensation,
CoBuyerAgentFirstName, 'ListAgentAOR', 'BuyerAgentAOR',
Unknown In Use Case
OriginalListPrice, CloseDate, ListPrice, DaysOnMarket, ContractStatusChangeDate,
PurchaseContractDate, ListingContractDate
Database
Management
ListingKey, PropertyType, ListingKeyNumeric, PropertySubType, ListingId,
BusinessType, 'latfilled', 'lonfilled'
Property Related
(Location)
Latitude, Longtitude, UnprasedAddress, MLSAreaMajor, CountyOrParish,
StreetNumberNumeric, City, StateOrProvince, PostalCode, MlsStatus
Property Related
(Property Attribute)
Flooring, ViewYN, WaterfrontYN, BasementYN, PoolPrivateYN, LivingArea,
FireplacesTotal, AboveGradeFinishedArea, AttachedGarageYN, ParkingTotal,
AssociationFeeFrenquency, TaxAnnualAmount, BuilderName, LotSizeAcres,
SubdivisionName, YearBuilt, BathroomsTotalInteger, TaxYear, BuildingAreaTotal,
BathroomsTotal, BelowGradeFinishedArea, CoveredSpaces, FireplaceYN, Stories,
Levels, LotSizeDimensions, LotSizeArea, MainLevelBdedrooms,
NewConstructionYN, GarageSpaces, AssociationFee, LotSizeSquareFeet
Property Related
(Neighborhood)
ElementarySchool, ElementarySchoolDistrict, MiddleOrJuniorSchool, HighSchool,
HighSchoolDistrict, MiddleOrJuniorSchoolDistrict

10. Variable Category Variables Included
Listing Related ListAgentEmail, ListAgentFirstName, ListAgentLastName, ListOfficeName, BuyerOfficeName, CoListOfficeName, CoListAgentFirstName, CoListAgentLastName, BuyerAgentFirstName,
BuyerAgentLastName, BuyerOfficeAOR, BuyerAgencyCompensationType, BuyerAgencyCompensation, CoBuyerAgentFirstName,'ListAgentAOR', 'BuyerAgentAOR',
Unknown In Use Case
OriginalListPrice, CloseDate, ListPrice, DaysOnMarket, ContractStatusChangeDate, PurchaseContractDate, ListingContractDate
Database Management ListingKey, PropertyType, ListingKeyNumeric, PropertySubType, ListingId, BusinessType, 'latfilled', 'lonfilled'
Property Related
(Location)
Latitude, Longtitude, UnprasedAddress, MLSAreaMajor, CountyOrParish, StreetNumberNumeric, City,
StateOrProvince, PostalCode, MlsStatus
Property Related
(Property
Attribute)
Flooring, ViewYN, WaterfrontYN, BasementYN, PoolPrivateYN, LivingArea, FireplacesTotal,
AboveGradeFinishedArea, AttachedGarageYN, ParkingTotal, AssociationFeeFrenquency, TaxAnnualAmount,
BuilderName, LotSizeAcres, SubdivisionName, YearBuilt, BathroomsTotalInteger, TaxYear, BuildingAreaTotal, BathroomsTotal,
BelowGradeFinishedArea, CoveredSpaces, FireplaceYN, Stories, Levels, LotSizeDimensions, LotSizeArea, MainLevelBdedrooms,
NewConstructionYN, GarageSpaces, AssociationFee, LotSizeSquareFeet
Property Related
(Neighborhood)
ElementarySchool, ElementarySchoolDistrict, MiddleOrJuniorSchool, HighSchool, HighSchoolDistrict, MiddleOrJuniorSchoolDistrict
Variable Selection
Selected Variables

11. Variable Selection
Variable Transformations
Feature Engineering

Data Densification

•Reduce sparsity: high percentage of missing value

•Increased signal strength: notable difference in close
price between Yes and No

•Reduce dimensionality: combining related feature into
one
AdditionalAttractionYN
At least one Yes among WaterfrontYN, BasementYN,
NewConstructionYN, ViewYN, AttachedGarageYN, PoolPrivateYN

HaveSchoolYN
At least one Yes among ElementaryarySchool, High School,
MiddleOrJuniorSchool

Feature Enhancement

•More intuitive: direct measure of building condition

•Consistent Interpretation: year built spans a wider and
wider range as by time goes by, but range of building
age is relatively consistent

•Increase simplicity: easier for normalization and
interpretation by model

BuildingAge
CloseDate - BuiltYear
PriceYear
Year of CloseDate
•Capturing market related macro trends

12. Variable Selection Feature Engineering Data Cleaning
Removal of invalid, outlier and NA values

•Dropped rows with NA for features with less than
1% NA
•Left other NA untouched for model specific handling
NA Handling
3
2
Parking Total
The total number of parking spaces included in the sale
• Excluded negative values with unclear meaning
• Removed values <= 0

Latitude
The geographic latitude of some reference point on the property
• Excluded values that are not within the boundary
of California
• Removed values < 32, values > 42

Longitude
The geographic longitude of some reference point on the property
• Excluded values that are not within the boundary
of California
• Removed values < - 124, values > - 144
Validity Check
 • Used IQR (The Interquartile Range) to identify
outliers
∘ IQR = Q3-Q1
∘ Outlier: values < Q1 - 1.5 * IQR & values > Q3 + 1.5 * IQR
∘ Applied IQR on 9 numeric features (excluding ClosePrice)
• Removed extreme small values with common sense
∘ values < 500 for LivingArea
∘ values < 800 for LotSizeSquareFeet
∘ values < 1000 for ClosePrice

• Removed 819,551 rows in total

1
Outlier Removal
1
2
3

13. Variable Selection Feature Engineering Data Cleaning
Distribution Plot of Cleaned Numeric Features
1,317,377

rows
16
variables
Final Cleaned Data

14. 03
Models

15. Model #1: LASSO
Model Introduction
Least Absolute
Shrinkage and Selection
Operator
Employs L1 regularization,
which adds the absolute
value of the coefficients as a
penalty term to the
loss function
Effectively eliminates less
important features
from the dataset
Shrinks the
coefficients and
helps reduce model
complexity and multi-
collinearity

16. Model #1: LASSO
Initial Baseline Model Result
MSE: 53,821,345,110
RMSE: 231,994
MAE: 170,289
MAPE: 40.011%
R
2
: 0.538

Hyperparameter Argument
alpha 10

17. Model #1: LASSO
Model Specific Data Prep
Dummy Encoding
Imputed missing
values in
'MainLevelBedrooms'
and 'Stories' with
their respective
medians
Categorical features
were dummy encoded
into booleans.
drop_first = False set
to retain the first level
of each variable.
Square root
transformed
'ClosePrice' in order to
normalize the
distribution.
Features
standardized using
StandardScaler to
have zero mean and
unit variance.
Imputation Variable Transformation
Standardization

18. Model #1: LASSO
Model Specific Data Prep
The original ClosePrice distribution
(skewed right)
The square root transformed ClosePrice distribution
(normalized)

19. Model #1: LASSO
Errors/Obstacles
-Repeated ConvergenceWarning
errors appeared due to the
multicollinearity between the
subsequent columns.
-In the presence of multicollinearity,
the LASSO algorithm can struggle
to converge to a solution because
the highly correlated predictors can
cause the updates to the
coefficients to reinforce each other
-In the new randomized update
process, the order in which the
coefficients are updated is
randomized in each iteration,
reducing the chance that dependent
subsequent beta updates will occur
-This significantly reduced the direct
and immediate impact of
subsequent correlated features on
each other, leading to a faster and
more stable convergence
SOLUTION:
selection = ‘random’

20. Model #1: LASSO
Hyperparameter Tuning
-GridSearchCV with 5-fold cross-validation was used to
determine the best combination of hyperparameters
-Parameter grid set to explore specific alpha values
-The optimal alpha value for the model is 0.008


Hyperparameter Argument
alpha parameter grid [8e-3, 9e-3]
max_iter 10,000
selection ‘random’
MSE: 37,992,585,620.648
RMSE: 194,916.868
MAE: 134,925.813
MAPE: 24.483%
R
2
: 0.670
Scatterplot suggests possible heteroscedasticity
within the data/limitations of the LASSO model

21. Model #1: LASSO
Final Model Result/Comparison
DIFFERENCE:
MSE: ↓15,828,759,489
RMSE: ↓37,077
MAE: ↓ 35,363
MAPE: ↓15.528%
R
2
↑0.132
INITIAL PERFORMANCE:
MSE: 53,821,345,110
RMSE: 231,994
MAE: 170,289
MAPE: 40.011%
R
2
: 0.538
BEST PERFORMANCE
MSE: 37,992,585,621
RMSE: 194,917
MAE: 134,926
MAPE: 24.483%
R
2
: 0.670

22. Model #1: LASSO
Feature Importance
Feature importance solely comprised of CountyOrParish variables

23. Model #1: LASSO
Performance on May Data
DIFFERENCE:
MSE: ↓ 83,738,617,008
RMSE: ↓ 130,317
MAE: ↓ 68,267
MAPE: ↓ 1.565%
R
2
↑ 0.068
INITIAL PERFORMANCE:
MSE: 149,340,590,134
RMSE: 386,446
MAE: 256,148
MAPE: 24.198%
R
2
: 0.536
BEST PERFORMANCE
MSE: 65,601,973,126
RMSE: 256,129
MAE: 187,881
MAPE: 22.633%
R
2
: 0.604
Current Stage
ClosePrice threshold: $1,910,000Max ClosePrice: $2,941,239
MODEL LIMITATION: High
ClosePrice values
LASSO Model performance
decreases at
ClosePrice > $1,910,000

24. The Random Forest Regressor is an ensemble learning method that constructs
multiple decision trees during training and outputs the average prediction of
the individual trees. It is particularly robust against overfitting and can handle
large datasets with high dimensionality effectively.
Model #2: Random Forest

25. ●Dropped 'MainLevelBedrooms'
●Filled missing values using median for numerical features and mode for
categorical features
●Scaled features using scaler created from train set to have mean 0 and unit
variance
●Applied TruncatedSVD with 40 components
Model Specific Data Adjustments

26. ●Initial Hyperparameters:
○n_estimators=1
○max_depth=10
● Performance Metrics:
○MSE: 40,646,488,216
○RMSE: 201,610
○MAE: 141,616
○MAPE: 0.2636
○R²: 0.6404
Baseline Result

27. ●Optimal Hyperparameters:
○n_estimators=200
○max_depth=20
● Performance Metrics:
○MSE: 26,576,669,599
○RMSE: 163,024
○MAE: 108,467
○MAPE: 0.1904
○R²: 0.7714
Optimal Result

28. Initial Evaluation:
●MSE: 141,419,942,050
●RMSE: 376,058
●MAE: 231,310
●MAPE: 0.1925
●R²: 0.5616

Model Result for May Test Data

Adjusted Evaluation:
Filtered out close price > $1,800,000
●MSE: 43,629,198,771
●RMSE: 208,876
●MAE: 146,837
●MAPE: 0.1673
●R²: 0.705

29. A tree-based ensemble learning model that builds trees sequentially, with each subsequent tree learning from the
errors made by the previous tree, collectively enhancing overall predictive accuracy and robustness
XGBoost
Baseline ResultXGBoost
Initial Fit
n_estimators = 500
learning_rate = 0.2
max_depth = 6
Baseline Result
1
2
Mean Squared Error (MSE) : 23,462,712,624
Root Mean Squared Error (RMSE) : 153,175
Mean Absolute Error (MAE): 108,165
Mean Percent Error (MAPE): 19.757%
R^2: 0.8016

Model #3: XGBoost
Iteration 1

30. Model TuningBaseline ResultXGBoost
Hyperparameter Tuning
Grid search with 2 fold cross validation
Model Set Up Hyperparameter Range
random_state = 42
n_estimators [300, 500, 700]
learning_rate [0.05, 0.1, 0.2]
max_depth [4, 6, 8, 10]
Model Set Up Hyperparameter Range
n_estimators = 700
learning_rate = 0.1
max_depth = 10
random_state = 42
subsample [0.7, 0.8, 0.9]
colsample_bytree [0.7, 0.8, 0.9]
gamma [0, 0.1, 0.2]
Model Set Up Hyperparameter Range
n_estimators = 700
learning_rate = 0.1
max_depth = 10
subsample = 0.9
colsample_bytree = 0.9
gamma = 0
random_state = 42
reg_alpha [0, 0.1, 0.5]
reg_lambda [0.5, 1.0, 1.5]
Not much improvement
3
Mean Squared Error (MSE) : 22,289,052,765
Root Mean Squared Error (RMSE) : 149,295
Mean Absolute Error (MAE): 104,467
Mean Percent Error (MAPE): 18.870%
R^2: 0.8116

31. Model TuningBaseline ResultXGBoost
Hyperparameter Tuning
Random search with 3 fold cross validation
Model Set Up Hyperparameter Range Best Value
random_state = 42
n_estimators randint(300, 1000) 912
learning_rate uniform(0.05, 0.15) 0.06
max_depth randint(4, 11) 10
subsample uniform(0.7, 0.3) 0.84
colsample uniform(0.7, 0.2) 0.74
gamma uniform(0, 0.2), 0.14
reg_alpha uniform(0, 0.5) 0.00
reg_lambda uniform(0.5, 1.0) 0.62
Not much improvement
Mean Squared Error (MSE) : 22,238,466,240
Root Mean Squared Error (RMSE) : 149,126
Mean Absolute Error (MAE): 104,472
Mean Percent Error (MAPE): 18.898%
R^2: 0.8120

3

32. Model TuningBaseline ResultXGBoost
Model specific data adjustments
Adding features
PriceYear: Capturing market related macro trends
NA removal
Dropped all rows with missing value in features with
< 1% NA
Feature Scaling
After train test split, scaled features using scaler
created from train set to have mean 0 and unit
variance

Hyperparameter Tuning
Model Set Up Hyperparameter Range
subsample=0.9,
colsample_bytree=0.9,
reg_alpha=0.5,
reg_lambda=1.5
random_state = 42
n_estimators [500, 700, 900, 1100]
learning_rate [0.01, 0.05, 0.1, 0.2]
max_depth [4, 6, 8, 10]
Experimentation on different evaluation metric for CV, different objective function, and different data splitting method
was done. None made notable difference.
4
5
Mean Squared Error (MSE) : 7,020,959,460
Root Mean Squared Error (RMSE) : 83,791
Mean Absolute Error (MAE): 53,877
Mean Percent Error (MAPE): 9.267%
R^2: 0.9386

Additional Outlier Removal
Based on common sense, removed following values
LotSizeSquareFeet <= 800
LivingArea <= 500
ClosePrice <= 10000
Iteration 2

33. Model TuningBaseline ResultXGBoost
Model Performance on May: notable decrease
6
Mean Squared Error (MSE) : 59,791,300,279
Root Mean Squared Error (RMSE) : 244,522
Mean Absolute Error (MAE): 137,427
Mean Percent Error (MAPE): 11.089%
R^2: 0.8148
Error Analysis on May Data
Lack of Training Data With High Close Price
Very high close prices were removed when using IQR to
remove outliers

34. Model TuningBaseline ResultXGBoost
Model Performance on May: seeking for break through point
6
Error Analysis on May Data
County Level Geographic Pattern
The model successfully captured geographic patterns at
the county level. However, it did not effectively learn
more localized, community-level geographic patterns
● Top 10 important features are all location related

●9 out of 10 important features originate from the
CountyOrParish variable


●Latitude and Longitude ranking not as high

35. Model TuningBaseline ResultXGBoost
Model specific data adjustments
Adding features
ZipCodeGroup: Label for which price group does this
zip code belongs to (0 is cheapest, 3 most expensive,
and 4 for no data for grouping )
NA Handling
Experimented with NA filling methods, and kept the
best method
FirePlaceYN fill NA with True
Stories fill NA with 1
MainLevelBedrooms fill NA with 1


7
Adjustment in Outlier Removal
Excluded ClosePrice from IQR
ClosePrice <= 20,000,000
Iteration 3
ZipCodeGroup
•Data source: Downloaded Zillow Home Value Index
(ZHVI) Data for Single family homes. Filtered to focus
on CA only. (ZHVI: A measure of the typical home value and market changes
across a given region and housing type)

•Zip Code Grouping: Used KNN model to cluster
California zip codes based on their 2023 ZHVI into four
distinct price groups. Used elbow plot to decide optimal K

•Stored Zip Code Grouping: create dictionary with key
being group label, and value being zip codes in this
group (No ZHVI value was stored!)

•Assign zip codes group to our data

Group 0: 688,078
Group 1: 31,358
Group 2: 215,914

Group 3: 478,636
Group 4: 1,268

36. Model TuningBaseline ResultXGBoost
Model Performance on May Data
8
Iteration 3
Mean Squared Error (MSE) : 59,791,300,279
Root Mean Squared Error (RMSE) : 244,522
Mean Absolute Error (MAE): 137,427
Mean Percent Error (MAPE): 11.089%
R^2: 0.8148
Mean Squared Error (MSE) : 52,502,317,194
Root Mean Squared Error (RMSE) : 229,134
Mean Absolute Error (MAE): 90,246
Mean Percent Error (MAPE): 9.234%
R^2: 0.8610
Main Takeaways
Our iteration 2 model is
lacking at higher close price;

Solutions
1. Including more higher close
price data points in training
2. Utilize geographical
information (zip code) more
effectively

37. Model TuningBaseline ResultXGBoost Mode Result
Evaluation metrics comparison
[Initial Model]
MSE : 23,462,712,624
RMSE : 153,175
MAE: 108,165
MAPE: 19.757%
R^2: 0.8016

[Iteration 2 Best Model]
MSE :7,020,959,460
RMSE : 83,791
MAE: 53,877
MAPE: 9.267%
R^2: 0.9386

[Final Best Model]
MSE : 23,607,658,735
RMSE : 153,648
MAE: 70,780
MAPE: 9.661%
R^2: 0.9227
Increase
Baseline
Accuracy
Increase
Prediction
Range

38. Model TuningBaseline ResultXGBoost Mode Result
Feature Importance comparison
More Balanced Feature Importance

39. 04
Results

40. Metrics Comparison
Models/MetricsMSE RMSE MAE MAPE R
2
LASSO 3.80*10^10 194,916.868 134,925.813 24.483% 0.670
RANDOM FOREST2.66*10^10 163,024 108,467 19.04% 0.7714
XGBOOST 2.36*10^10 153,648 70,780 9.66% 0.9227
XGBOOST: Demonstrates strong performance with smallest errors, and highest R-squared value, signifying a robust
fit to the data and excellent explanatory power.

41. Common Features
Fig.1: Top 20 Important Features of LASSO Fig.2: Top 20 Important Features of XGBoost
“CountyOrParish_Santa Clara,” “CountyOrParish_Alameda,” and “CountyOrParish_San Diego” are consistently ranked
among the top 10 important features in both models.
●This indicates their significant influence on the model's predictions. We will explore these features further in the
next steps to understand their impact better.
●The “CountyOrParish” variable, which refers to the location of the house, turns out to be an important feature in
determining house prices.

42. Future Steps
05

43. Future Steps
Pipeline Construction
-Implement automated data
update processes on a
monthly basis
-Ensure new data is
automatically tested
against the model and
report the latest model
performance
-Integrate new data into the
whole training set after
passing through same data
prep steps


Explore Network Models
-Explore more advanced
network based models
-Plan to investigate into
neural networks, such as
Recurrent Neural Networks
(RNN), Convolutional Neural
Networks (CNN), Transformer
Networks and so on…
-Enhance predictive accuracy
in large, high-dimensional
datasets
-Model performance drops for May
data due to very high close price
-Originally our training data used
IQR to remove outliers for all
numeric variables. We are now
excluding Close Price from the IQR
process and aim to set a cutoff at
2e7 across all three models to
increase model training exposure
on high close prices that were
originally removed via IQR.
-Goal: Restore the model
performance to similar level as
before
Error Analysis

44. CREDITS: This presentation template was created by
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Thank you!

45. CREDITS: This presentation template was created by
Slidesgo, and includes icons by Flaticon, and infographics
& images by Freepik

46. CREDITS: This presentation template was created by
Slidesgo, and includes icons by Flaticon, and infographics
& images by Freepik

47. Project activities
ActivityStart dateEnd dateResourceCostRevenue
Market research1/1/20XX 1/15/20XX
Market
research firm
$20,000
Product
development
1/16/20XX 6/30/20XX R&D team $200,000
Beta testing 7/1/20XX 8/15/20XX Beta testers $10,000
Marketing
campaign
1/1/20XX 1/15/20XX Advertising agency$100,000
Product launch 1/16/20XX 6/30/20XX Sales team $50,000 $500,000
Post-launch
support
7/1/20XX 8/15/20XX Support team $50,000 $800,000

48. What to show in
a mockup
1.Product/website description: A brief overview of the
product/website, including its key features,
dimensions, and materials used
2.Features and benefits: A detailed explanation of the
product's/website's features and how they will benefit
the user
3.Technical specifications: A list of the
product's/website's technical specifications, such as
dimensions, weight, power requirements, connectivity
options and hosting platform

49. Project timeline
Conduct market research
and analyze the target
audience to identify
opportunities
Create the product or
service by defining
features, design, and
functionality
Test the product with a
group of users to receive
feedback and improve
the product
Execute a marketing
campaign to promote the
product to the target
audience
Release the product or
service to the market
with a comprehensive
launch plan
Provide customer
support to ensure
customer satisfaction
Explore opportunities for
growth and expansion of
the product or service
Continuously improve the
product based on
customer feedback and
market trends

50. Project roadmap
Initiative Objective JanFebMarAprMayJunJulAugSepOctNovDec
Market research
Increase brand awareness by 30%
within the first year

Product development
Achieve a customer satisfaction
rate of 90%

Marketing campaign
Increase revenue by 20% within the
first year

Product launch
Achieve a customer satisfaction
rate of 90% within the first year

Expansion
Reduce costs by 15% within the first
year by expanding the product

Partnership
Reach profitability within 18
months of launch

51. Future Steps
In the future…
ideas
●

52. Project expenses
Labor
Materials
Equipment
Overhead
Other
Follow the link in the graph to modify its data and then paste the new one here. For more info, click here

53. KPI dashboard
Resource Utilization
rate
Cost per
unit
Labor 85% $50
Equipment70% $100
Materials 95% $20
Rent 90% $1,000
Energy 80% $80
Software
licenses
80% $200
Advertising60% $500
Follow the link in the graph to modify its data and then paste the new one here. For more info, click here
120 u/day2h
Output per worker Time to complete a task

54. Alternative resources
Here’s an assortment of alternative resources whose
style fits that of this template:
Vectors
●Abstract landing page
●Abstract lineal shapes landing page template

55. Data Prep and Rationale
____ ____
The project team is responsible
for the successful execution of
the project. Our team is
composed of experienced
professionals with the
necessary skills and expertise
to complete the project on time
and within budget
Our project budget is
$100,000. This budget includes
all expenses associated with
the project, including salaries
and equipment. We have
allocated these resources to
ensure that we are able to
complete the project within
budget
Our project requires a number
of specialized pieces of
equipment. We will be using
[insert equipment and their
functions]. All equipment is in
good working condition and
has been tested and calibrated
prior to use
____

56. The goal
Goals inside a company are usually
specific and measurable, with
clearly defined deadlines and
outcomes. The company’s goals
help focus the actions of the
organization and ensure resources
are used effectively
An aim in a corporate context is a
goal or desired result that the
organization seeks to achieve. The
aim should be clear and
achievable, and often serves as the
basis for further planning actions
inside the organization
Our aim

57. Equipment and materials
Funding for a company can come from personal
savings or investments, bank loans and other
loan options, venture capital and angel
investors, grants, competitions or programs,
crowdfunding…
Personnel costs refer to the expenses incurred
in hiring, training and retaining staff for a
company. This can include salaries, bonuses,
benefits and other payroll-related costs
Equipment and materials costs refer to all the
expenses related to the purchasing,
maintenance and upkeep of any physical items
used in production or other business processes
inside the company
Travel and miscellaneous costs refer to
expenses related to any travel-related
activities, such as conferences, trainings or
business trips. It may also include office
supplies, communications services, licenses
and other miscellaneous expenses
Sources of funding Personnel costs
Travel and miscellaneous

58. Implementing a new CRM
(Customer Relationship
Management) system to
improve customer data
management and
sales tracking
Outsourcing specific
business functions (such as
accounting or IT) to a
third-party provider to
reduce some costs and
increase time efficiency
Developing a new product
or service to diversify the
business and increase
revenue streams
Implementing a cost-saving
initiative, such as
energy-efficient practices
or process automation, to
reduce expenses
Solution 1 Solution 2 Solution 3
Launching an e-commerce
platform to expand the
reach of the business and
increase online sales
Establishing strategic
partnerships with other
businesses to gain access
to new markets or
innovative technologies
Solution 4 Solution 5 Solution 6

59. Instructions for use
If you have a free account, in order to use this template, you must credit Slidesgo by keeping the Thanks slide. Please
refer to the next slide to read the instructions for premium users.
As a Free user, you are allowed to:
●Modify this template.
●Use it for both personal and commercial projects.
You are not allowed to:
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For more information about editing slides, please read our FAQs or visit our blog:
https://slidesgo.com/faqs and https://slidesgo.com/slidesgo-school

60. As a Premium user, you can use this template without attributing Slidesgo or keeping the Thanks slide.
You are allowed to:
●Modify this template.
●Use it for both personal and commercial purposes.
●Hide or delete the “Thanks” slide and the mention to Slidesgo in the credits.
●Share this template in an editable format with people who are not part of your team.
You are not allowed to:
●Sublicense, sell or rent this Slidesgo Template (or a modified version of this Slidesgo Template).
●Distribute this Slidesgo Template (or a modified version of this Slidesgo Template) or include it in a database or in
any other product or service that offers downloadable images, icons or presentations that may be subject to
distribution or resale.
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Template.
●Register any of the elements that are part of this template as a trademark or logo, or register it as a work in an
intellectual property registry or similar.

For more information about editing slides, please read our FAQs or visit our blog:
https://slidesgo.com/faqs and https://slidesgo.com/slidesgo-school
Instructions for use (premium users)

61. This presentation has been made using the following fonts:
Space Grotesk
(https://fonts.google.com/specimen/Space+Grotesk)

Cairo
(https://fonts.google.com/specimen/Cairo)

#241160 #ffffff #e2e4fc #8861f1
#0b9cdc #01cfe6#545eea
Fonts & colors used

62. Create your Story with our illustrated concepts. Choose the style you like the most, edit its colors, pick the background
and layers you want to show and bring them to life with the animator panel! It will boost your presentation. Check out how
it works.
Storyset
Pana Amico Bro Rafiki Cuate

63. You can easily resize these resources without losing quality. To change the color, just ungroup the resource and click on
the object you want to change. Then, click on the paint bucket and select the color you want. Group the resource again
when you’re done. You can also look for more infographics on Slidesgo.
Use our editable graphic resources...

66. JANUARY FEBRUARY MARCH APRIL
PHASE 1
Task 1
Task 2
JANUARYFEBRUARY MARCH APRIL MAY JUNE
PHASE 1
PHASE 2
Task 1
Task 2
Task 1
Task 2

69. You can resize these icons without losing quality.
You can change the stroke and fill color; just select the icon and click on the paint bucket/pen.
In Google Slides, you can also use Flaticon’s extension, allowing you to customize and add even more icons.
...and our sets of editable icons

70. Educational Icons Medical Icons

71. Business Icons Teamwork Icons

72. Help & Support Icons Avatar Icons

73. Creative Process Icons Performing Arts Icons

74. Nature Icons

75. SEO & Marketing Icons