The waste water management project aims to monitor and optimize the treatment and disposal of wastewater in USA. The project involves multiple wastewater treatment plants, sources of waste water across various locations. The main objective is to ensure efficient treatment of wastewater
- Ensure efficient and responsible treatment of wastewater to improve effectiveness and efficiency.
- Integrate data from different sources and performing analysis.
- Design Analytical Report.
- Provide Recommendations based on analytical report.
1. Data Collection and observation: The project emphasis lies on collecting data related to treatment Plants, sources of waste water, and treatment results. These metrics are used for analyzing the overall performance of treatment facilities.
2. Performance Analysis: The project aims to evaluate the performance of different treatment facilities. It seeks to identify areas of improvement, optimize treatment efficiency, and enhance the overall effectiveness of wastewater management systems.
3. Analysis of Waste Water Sources: The project recognizes the importance of understanding the sources of wastewater and their specific characteristics. It aims to analyze wastewater based on different sources such as residential, healthcare, or industrial to identify any specific treatment requirements.
This project I worked on, taught me several things:
1. Working with Pandas Library:
- Reading data from CSV files using
pd.read_csv() - Checking information about the data using
df.info() - Handling null values and duplicates using
df.isnull().sum()anddf.duplicated().sum() - Converting data types using
pd.to_datetime()and method chaining - Deriving new columns from existing data
- Merging/joining dataframes using
pd.merge() - Grouping data and performing aggregations like
sum(),mean()
2. Data Visualization with Plotly:
- Creating bar charts using
px.bar() - Creating pie/donut charts using
px.pie() - Customizing chart layouts, titles, axes, legends
- Sorting and filtering data for visualization
- Creating line charts using
px.line()
3. Data Manipulation:
- Sorting dataframes using
sort_values() - Applying functions like
round()to columns - Performing arithmetic operations on columns
4. Calculating Key Performance Indicators (KPIs):
- Calculating the volume of water treated by treatment plants
- Calculating the contribution of waste water from different sources
- Calculating the utilization of treatment plants based on capacity
- Calculating the efficiency of treatment plants based on successful treatment
5. Working with Datetime:
- Extracting day names from datetime objects using
dt.day_name() - Grouping and aggregating data based on dates
1. Data Consolidation and Transformation: Consolidation of relevant data sources, such as wastewater treatment plant data, sources of waste water data into a structured format suitable for analysis.
2. Key Performance Indicators (KPIs): Identify key performance indicators related to wastewater management.
3. Analytical Report: Creation analytical reports that provide insights into wastewater treatment performance. These visualizations are helpful in monitoring KPIs, identify trends, and to make data-driven decisions for process optimization.
4. Recommendations: Recommendations based on data analysis to optimize wastewater treatment processes, minimize resource consumption, and enhance overall efficiency
1. Data Quality and Availability: The risk of incomplete, inaccurate, or inconsistent data could impact the reliability and validity of the analytics outcomes. Inefficient data collection procedures or data integration challenges could hamper project chances of success.
2. Protection of Data: Dealing with confidential wastewater data requires rigorous data security measures. Inadequate data protection practices, unauthorized access, or data breaches could compromise the confidentiality and privacy of the data, leading to legal and reputational risks.
3. Interpretation and Decision-making: The interpretation of analytics results requires expertise and understanding of context. Misinterpretation of data, biases, or incorrect decision-making could lead to ineffective outcomes.
4. Budget & Resource Constraints: Insufficient resources, both financial and human, could impact the project's scope, timeline, and quality
Below you can find a few snippets of the project :
