MPVII

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RETAIL SALES PREDICTIONMini Project

• By -
  
  Under Supervision of -
• Pranav Killedar,
  Mrs.
  Sophiya Shikalgar
• Sandesh More,
• Sumesh Patil.

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Introduction -

• Retail sales prediction project is to develop a
  model that can accurately forecast future sales
  for a given product or a store.
• Machine learning and Deep learning can help us
  discover the factors that influence sales in a
  retail store and estimate the number of sales in
  the near future.
• This not only leads to customer satisfaction but
  also builds brand loyalty, a valuable asset in
  today's competitive market.
• Retail sales forecasting is an essential task for
  the management of a store.

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Problem Statement -

• Predicting future sales of a retail store based
  on historical sales data and other relevant
  factors such as promotions, competition,
  seasonality, and locality.
• Developing a recommendation system for a retail
  store to suggest products to customers based on
  their behavioral data and purchase history.
• Optimizing retail prices using machine learning
  models to maximize profit and improve customer
  experience.

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Objective -

• Sales Forecasting To accurately predict future
  sales volumes for specific products, stores, or
  market segments.
• Demand Planning To anticipate and respond to
  shifts in consumer demand on time.
• Inventory Management To minimize overstock and
  understock situations, optimizing stock levels to
  reduce carrying costs while meeting customer
  demand effectively.
• Pricing Strategy To aid in setting competitive
  and dynamic pricing strategies based on demand
  forecasts, competitor behavior, and economic
  factors.

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Methodology -

• 1. Problem Definition and Scope Begin by clearly
  defining the objectives of the sales prediction
  project. Identify the business goals and the key
  questions the predictions should answer.
• 2. Data Collection and Preparation Gather
  historical sales data, which should include
  relevant attributes such as date, product
  details, sales figures, and external factors like
  economic indicators and weather data.
• 3. Exploratory data analysis It helps you
  understand your data, identify patterns,
  outliers, and relationships among variables.

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Methodology -

• 4. Feature Engineering Create features that can
  influence sales predictions, such as lagged
  sales, seasonal indicators, holidays, and
  promotional events.
• 5. Model Selection Choose appropriate predictive
  modeling techniques based on the project's
  objectives. Options include time series analysis,
  regression, machine learning algorithms, and deep
  learning models.
• 6. Model Development Develop and build the
  chosen predictive models using the preprocessed
  data. Ensure that model architecture,
  hyperparameters, and optimization strategies are
  in line with the project's objectives.

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Methodology -

• 7. Training and Validation Split the dataset
  into training and validation sets to train and
  fine-tune the models.
• 8. Hyperparameter Tuning Optimize model
  hyperparameters and configurations to enhance
  predictive accuracy.
• 9. Evaluation Assess the model's performance
  using a separate test dataset, ensuring that it
  generalizes well to unseen data. Refine the model
  if necessary based on the evaluation results.

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System Requirements -

• Hardware Requirements -
• Sufficient processing power (multi-core CPU or
  cloud-based computing) for modeling and analysis.
• Adequate memory (8GB or more) for handling data
  and computational demands.
• Sizable storage capacity (1TB or more) for
  dataset storage and model parameters.
• Software Requirements -
• Utilize the Python programming language for its
  rich ecosystem in data science.
• Depend on data analysis and machine learning
  libraries, such as NumPy, pandas, scikit-learn,
  Matplotlib and seaborn
• Consider deep learning frameworks like TensorFlow
  or PyTorch for advanced predictive models.

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Advantages and Disadvantages -

• Advantages
• Efficient Supply Chain
• Enhanced Pricing Strategies
• Improved Customer Experience
• Effective Promotion Planning
• Disadvantages
• Inaccuracies
• Complexity
• Data Quality
• External Factors

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Conclusion -

• Retail sales predictions are a vital asset for
  the modern retail landscape, offering the promise
  of optimized operations, improved customer
  experiences, and enhanced profitability.
• However, these predictions are not without their
  challenges. Accuracy hinges on high-quality data
  and the adaptation to unpredictable external
  factors. The cost and complexity of implementing
  and maintaining prediction models can be
  significant, particularly for smaller retailers.
• In conclusion, the benefits of retail sales
  predictions are substantial, but success requires
  a careful balance of technology, data quality,
  and ethical considerations.

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THANK YOU