ALY6040_FinalProject_GROUP5
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Northeastern University *
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Course
6040
Subject
Geography
Date
Dec 6, 2023
Type
docx
Pages
10
Uploaded by PresidentParrot3945
Final Project
Group 5
Shikha Sharma
Vrinda Shah
Jeet Parek
Kashish Shah
Parth Mistry
Story that the data is revealing
Initially, our business question was focused on understanding the relationship between weather
conditions and temperature. We started by analyzing the "Summary" column to gain a high-level
understanding of different weather patterns. This helped us identify the types of weather
conditions recorded in the dataset, such as clear, cloudy, rainy, or snowy.
We then examined the "Temperature (C)" and "Apparent Temperature (C)" columns to compare
actual and perceived temperatures. By applying Random Forest Regression, we were able to
predict temperatures based on other variables in the dataset, identifying the most significant
factors influencing temperature changes.
To visualize temperature trends over time, we created a "Temperature over Time" graph. This
graph allowed us to observe seasonal patterns, temperature fluctuations, and identify any long-
term trends or anomalies.
In order to gain deeper insights, we performed feature engineering to extract more meaningful
information from the existing columns. For example, we extracted the month from the
"Formatted Date" column to analyze weather patterns across different months of the year.
To understand the relationships between variables, we constructed a correlation matrix. This
matrix helped us identify variables that were strongly correlated with temperature, such as
humidity, wind speed, and pressure. This information provided insights into the factors
influencing temperature variations.
Furthermore, we applied cluster analysis techniques to group similar weather conditions based
on multiple variables. This helped us identify distinct weather patterns and understand how they
relate to temperature changes.
Throughout the project, we observed several interesting findings. We discovered that temperature
variations were strongly correlated with changes in humidity, wind speed, and pressure.
Additionally, certain weather conditions, such as high humidity and low visibility, were
associated with lower temperatures. The analysis also revealed distinct weather patterns across
different seasons and months.
These insights have implications for various industries. For example, businesses in the tourism
sector can leverage this information to optimize marketing campaigns based on weather
conditions during different seasons. Agricultural industries can use these insights to plan crop
planting and harvesting schedules, considering optimal temperature ranges.
In conclusion, our analysis of the "weatherhistory" dataset has provided valuable insights into the
relationship between weather conditions and temperature. By applying various techniques such
as regression analysis, visualization, feature engineering, correlation analysis, and cluster
analysis, we have uncovered patterns and correlations that contribute to a deeper understanding
of weather dynamics. These insights can guide decision-making in various sectors and assist in
optimizing strategies based on weather conditions.
Analysis Report
1. Introduction
The purpose of this report is to present the analysis conducted on the "weatherhistory" dataset to
gain insights into the relationship between weather conditions and temperature. The analysis
aimed to answer questions regarding the impact of different weather factors on temperature
variations. This report outlines the steps taken, methodologies used, tools and techniques
employed, and the results obtained during the analysis process.
2. Methodologies and Tools Used
2.1 Data Preparation and Exploration
Loaded the dataset and checked for missing values and data quality issues.
Explored the dataset to understand the available columns and their meanings.
Performed data cleaning and formatting as necessary.
2.2 Analysis Techniques
Descriptive statistics: Calculated basic statistics (mean, standard deviation, minimum,
maximum) for temperature and other variables of interest.
Data visualization: Created various graphs and plots to visualize temperature trends,
weather patterns, and relationships between variables.
Random Forest Regression: Utilized this regression technique to predict temperatures
based on other variables in the dataset.
Feature engineering: Extracted additional features from existing columns to capture more
meaningful information.
Correlation analysis: Calculated correlation coefficients to identify relationships between
temperature and other variables.
Cluster analysis: Applied clustering algorithms to group similar weather conditions.
2.3 Tools Used
Python: Leveraged the Python programming language for data manipulation, analysis,
and visualization.
Libraries: Utilized libraries such as pandas, matplotlib, seaborn, scikit-learn, and scipy
for data analysis and modeling.
3. Analysis Process and Results
3.1 Data Exploration and Descriptive Statistics
Examined the dataset's columns and their meanings.
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Checked for missing values and data quality issues.
Calculated descriptive statistics for temperature and other variables.
3.2 Data Visualization
Created a "Temperature over Time" graph to visualize temperature trends across the
dataset's timeframe.
Plotted various graphs to explore relationships between temperature and weather factors
like humidity, wind speed, and pressure.
Visualized weather patterns using bar charts, histograms, and heatmaps
3.3 Random Forest Regression
Applied Random Forest Regression to predict temperature based on other variables.
Identified the most significant factors influencing temperature changes.
3.4 Feature Engineering
Extracted the month from the "Formatted Date" column to analyze weather patterns
across different months of the year.
Created additional features to capture time-related information and weather conditions.
3.5 Correlation Analysis
Calculated correlation coefficients to measure the strength and direction of relationships
between temperature and other variables.
Identified variables strongly correlated with temperature.
3.6 Cluster Analysis
Conducted cluster analysis to group similar weather conditions based on multiple
variables.
Examined distinct weather patterns and their relationship with temperature.
4.Insights and Findings
4.1 Results of Techniques
The Random Forest Regression model accurately predicted temperatures based on
weather factors.
Correlation analysis revealed strong relationships between temperature and variables
such as humidity, wind speed, and pressure.
Cluster analysis identified distinct weather patterns associated with temperature
variations.
4.2 New Insights
High humidity and low visibility were associated with lower temperatures.
Weather patterns exhibited seasonality, and temperature variations were observed across
different months.
4.3 Impact on Initial Questions
The analysis provided insights into the impact of weather factors on temperature
variations.
The initial questions were answered by uncovering the relationships between temperature
and various weather conditions.
As the analysis progressed, additional questions emerged, leading to a deeper exploration
of weather patterns and their influence on temperature.
Visualizations
Figure1 : Bar plot of avg temp vs summary
The code generates a bar chart that displays the average temperature for different weather summaries.
Each bar represents a weather summary category, and the height of the bar indicates the average
temperature for that category in Celsius.
The x-axis represents the weather summary categories, and the y-axis represents the average temperature
in Celsius. The chart allows you to compare the average temperatures across different weather summary
categories.
The chart shows the distribution of average temperatures for each weather summary category. By looking
at the chart, you can identify the weather summaries with higher or lower average temperatures. The
labels on the x-axis are rotated 90 degrees to accommodate the potentially long weather summary names.
This visualization helps in understanding the relationship between weather summaries and average
temperatures, providing a quick overview of the temperature patterns associated with different weather
conditions.For instance we can see that when the weather is dry temperture is the high and when the
weather is breezy and foggy temperature is the lowest.
Figure 2: Scatter plot of the temperature and weather factors
The code generates a scatter plot that shows the relationships between temperature and various weather
factors. The plot includes four weather factors: temperature itself, humidity, wind speed, and pressure.
Each weather factor is represented by a separate scatter plot.
The x-axis represents the values of the weather factors, while the y-axis represents the temperature in
Celsius. Each data point in the scatter plot represents a specific measurement or observation. The position
of the data point indicates the values of the weather factor and the corresponding temperature.
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By examining the scatter plot, you can identify any patterns or relationships between the weather factors
and temperature. For example, you can observe if there are any trends or correlations between higher
humidity or wind speed values and temperature. Additionally, you can also determine if there are any
outliers or clusters that might indicate specific weather conditions.
The legend shows the labels of the weather factors, making it easier to distinguish between the different
scatter plots. This allows for a clear understanding of which weather factor each scatter plot represents.
This visualization helps in visualizing the relationships between temperature and various weather factors,
providing insights into how these factors might influence or be related to temperature variations.
Figure 4: Heat of temperature and weather factors
The above heatmap showing
the similar relationshipas shown in scatter plot for temperature and weather
factors. In heatmap it is easy to read the relationship clearly. We can see that there is a strong positive
relationship between Temperature and the Apparent temperature, strong negative relationship between
Temperature and Pressure variable.
The code generates a correlation heatmap using the seaborn library. The heatmap provides a visual
representation of the correlation matrix calculated from the selected weather factors.
The selected weather factors for the analysis are:
Temperature (C)
Apparent Temperature (C)
Humidity
Wind Speed (km/h)
Visibility (km)
Pressure (millibars)
The correlation matrix measures the strength and direction of the linear relationship between pairs of
variables. Each cell in the heatmap represents the correlation coefficient between two weather factors.
The values range from -1 to 1, indicating the strength and direction of the correlation.
Interpreting the heatmap:
Darker shades (closer to -1 or 1): indicate a stronger negative or positive correlation, respectively. For
example, a dark blue color suggests a strong negative correlation, meaning that as one weather factor
increases, the other decreases, and vice versa. A dark red color suggests a strong positive correlation,
meaning that as one weather factor increases, the other also increases, and vice versa.
Lighter shades (closer to 0): indicate a weaker or no correlation between the variables.
By examining the heatmap, you can identify which weather factors have a significant correlation with
each other. For example, if you observe a dark blue color between "Temperature (C)" and "Apparent
Temperature (C)", it suggests a strong positive correlation between these two variables. This indicates that
as the actual temperature increases, the perceived temperature also tends to increase.
Similarly, you can analyze the relationships between other weather factors. The annotations in the
heatmap display the numerical correlation coefficients, providing additional information about the
strength of the correlations.
This visualization helps in understanding the interdependencies and relationships between different
weather factors, enabling insights into how they relate to each other in the dataset.
5.Conclusion
The analysis of the "weatherhistory" dataset using various methodologies, tools, and techniques provided
valuable insights into the relationship between weather conditions and temperature. The results
highlighted the impact of variables such as humidity, wind speed, and pressure on temperature variations.
The analysis also revealed distinct weather patterns across different months. These findings contribute to
a better understanding of weather dynamics and can assist in decision-making processes across various
industries.
6.Recommendations
Based on the analysis, it is recommended to consider the identified weather factors when planning
activities or making decisions sensitive to temperature variations. Further research and analysis could
focus on exploring the long-term trends and predicting temperature changes based on weather conditions.
Additionally, incorporating real-time data sources and expanding the analysis to include a larger
geographic area could provide more comprehensive insights into temperature variations and weather
patterns.
Overall, this analysis report provides valuable insights into the relationship between weather conditions
and temperature and serves as a foundation for informed decision-making in various sectors influenced by
weather dynamics.
Based on all the interpretations from the previous analyses, here are some recommendations
for next actions:
Temperature and Weather Summary
: The bar chart of average temperature by weather summary
showed distinct temperature differences among different weather conditions. It would be beneficial to
further investigate the relationship between temperature and weather summary. Incorporating additional
variables such as precipitation, cloud cover, or wind direction could provide more insights into how
different weather conditions impact temperature.
Temperature and Weather Factors
: The scatter plot examining the relationship between temperature and
weather factors (humidity, wind speed, and pressure) revealed some interesting patterns. Further analysis
can be done to explore the individual and combined effects of these factors on temperature. It is
recommended to incorporate additional explicit variables such as dew point, wind direction, or cloud
cover to better understand the influence of various weather factors on temperature.
Correlation Analysis
: The correlation heatmap provided insights into the relationships among weather
factors. In future analysis, it would be valuable to consider these correlations and explore how multiple
factors interact to influence temperature. For example, investigating the combined effects of humidity,
wind speed, and pressure on temperature using regression models or machine learning techniques can
provide a more comprehensive understanding of their impact.
Seasonal Analysis
: Considering the potential impact of seasons on temperature is crucial. By
incorporating explicit variables such as month or season, it is possible to identify seasonal patterns and
trends in temperature. This analysis can help identify any variations in the relationships between
temperature and other weather factors across different seasons.
Overall, incorporating additional explicit variables such as precipitation, cloud cover, wind direction, dew
point, or season can enrich the analysis and provide a more comprehensive understanding of the factors
influencing temperature. These variables can help uncover more nuanced relationships and patterns,
leading to valuable insights in weather analysis and forecasting.
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